Shelter-shrub Project
Nargol Ghazian
2024-02-27
library (ggplot2)
library (tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary (dplyr)
library(patchwork)###load photo data for each region and year
mojave_2022 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Mojave cams 2022.csv")
carrizo_2022 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Carrizo cams 2022.csv")
mojave_2023 <- read.csv ("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Mojave cams 2023.csv")
carrizo_2023 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Carrizo cams 2023.csv")
mojave_winter2023 <-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Mojave cams winter 2023.csv")
carrizo_winter2023 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Carrizo cams winter 2023.csv")
###calculate capture rate for each site and year from Wildlifeinsight
### 0.0966% capture rate for Mojave 2022
### 0.0538% capture rate for Mojave 2023 winter
### 1.6% capture rate for Carrizo 2022
### 0.923% capture rate for Carrizo 2023 winter
### 0.19% capture rate for Mojave 2023
### 0.46% capture rate for Carrizo 2023### Total Observations is species list to make percent proportion plots
### 2022 spring-summer Carrizo
total_observations_carrizo2022 <- carrizo_2022 %>% group_by(scientific_name) %>% summarise(total = sum(animal_hit)) %>% filter(scientific_name != "No CV Result No CV Result")
animals_by_microsite_carrizo2022 <- carrizo_2022 %>% group_by(microsite, scientific_name) %>% summarise(captures = sum(animal_hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.microsite_obvs_carrizo2022 <- merge(animals_by_microsite_carrizo2022, total_observations_carrizo2022, all = TRUE)
percent_proportion_carrizo2022<-microsite_obvs_carrizo2022 %>% mutate(percent_proportion = (captures/total))
A<-ggplot(percent_proportion_carrizo2022, aes(factor(scientific_name), percent_proportion, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Relative Proportion") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo Spring 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) ## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.### 2022 spring-summer Mojave
total_observations_mojave2022<- mojave_2022 %>% group_by(scientific_name) %>% summarise(total = sum(animal_hit)) %>% filter(scientific_name != "No CV Result No CV Result")
animals_by_microsite_mojave2022 <- mojave_2022 %>% group_by(microsite, scientific_name) %>% summarise(captures = sum(animal_hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.microsite_obvs_mojave2022 <- merge(animals_by_microsite_mojave2022, total_observations_mojave2022, all = TRUE)
percent_proportion_mojave2022<-microsite_obvs_mojave2022 %>% mutate(percent_proportion = (captures/total))
B<-ggplot(percent_proportion_mojave2022, aes(factor(scientific_name), percent_proportion, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Relative Proportion") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave Spring 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))
### 2023 spring-summer Carrizo
total_observations_carrizo2023 <- carrizo_2023 %>% group_by(scientific_name) %>% summarise(total = sum(animal_hit)) %>% filter(scientific_name != "No CV Result No CV Result")
animals_by_microsite_carrizo2023 <- carrizo_2023 %>% group_by(microsite, scientific_name) %>% summarise(captures = sum(animal_hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.microsite_obvs_carrizo2023 <- merge(animals_by_microsite_carrizo2023, total_observations_carrizo2023, all = TRUE)
percent_proportion_carrizo2023<-microsite_obvs_carrizo2023 %>% mutate(percent_proportion = (captures/total))
C<-ggplot(percent_proportion_carrizo2023, aes(factor(scientific_name), percent_proportion, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Relative Proportion") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo Spring 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))
### 2023 spring-summer Mojave
total_observations_mojave2023<- mojave_2023 %>% group_by(scientific_name) %>% summarise(total = sum(animal_hit)) %>% filter(scientific_name != "No CV Result No CV Result")
animals_by_microsite_mojave2023 <- mojave_2023 %>% group_by(microsite, scientific_name) %>% summarise(captures = sum(animal_hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.microsite_obvs_mojave2023 <- merge(animals_by_microsite_mojave2023, total_observations_mojave2023, all = TRUE)
percent_proportion_mojave2023<-microsite_obvs_mojave2023 %>% mutate(percent_proportion = (captures/total))
D<-ggplot(percent_proportion_mojave2023, aes(factor(scientific_name), percent_proportion, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Relative Proportion") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave Spring 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))
### 2023 winter Carrizo
total_observations_carrizo2023_winter <- carrizo_winter2023 %>% group_by(scientific_name) %>% summarise(total = sum(animal_hit)) %>% filter(scientific_name != "No CV Result No CV Result")
animals_by_microsite_carrizo2023_winter <- carrizo_winter2023 %>% group_by(microsite, scientific_name) %>% summarise(captures = sum(animal_hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.microsite_obvs_carrizo2023_winter <- merge(animals_by_microsite_carrizo2023_winter, total_observations_carrizo2023_winter, all = TRUE)
percent_proportion_carrizo2023_winter<-microsite_obvs_carrizo2023_winter %>% mutate(percent_proportion = (captures/total))
E<-ggplot(percent_proportion_carrizo2023_winter, aes(factor(scientific_name), percent_proportion, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Relative Proportion") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))
### 2023 winter Mojave
total_observations_mojave2023_winter <- mojave_winter2023 %>% group_by(scientific_name) %>% summarise(total = sum(animal_hit)) %>% filter(scientific_name != "No CV Result No CV Result")
animals_by_microsite_mojave2023_winter <- mojave_winter2023 %>% group_by(microsite, scientific_name) %>% summarise(captures = sum(animal_hit))## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.microsite_obvs_mojave2023_winter <- merge(animals_by_microsite_mojave2023_winter, total_observations_mojave2023_winter, all = TRUE)
percent_proportion_mojave2023_winter<-microsite_obvs_mojave2023_winter %>% mutate(percent_proportion = (captures/total))
F<-ggplot(percent_proportion_mojave2023_winter, aes(factor(scientific_name), percent_proportion, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Relative Proportion") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))
A/B
C/D
E/F
### PCOA
library(permute)
library(vegan)## Loading required package: lattice## This is vegan 2.6-4library(tidyverse)
library(tidyr)
library(ape)##
## Attaching package: 'ape'## The following object is masked from 'package:dplyr':
##
## where### PCOA Carrizo 2022
pca_data_final_carrizo2022 <- microsite_obvs_carrizo2022%>%
spread(scientific_name, captures) %>%
replace(is.na(.),0)%>% ungroup() %>% dplyr::select(-microsite)
adonis(pca_data_final_carrizo2022 ~ microsite, data = microsite_obvs_carrizo2022)## 'adonis' will be deprecated: use 'adonis2' instead## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 3 0.3019 0.10062 0.31542 0.03058 0.992
## Residuals 30 9.5703 0.31901 0.96942
## Total 33 9.8721 1.00000
##
## $call
## adonis(formula = pca_data_final_carrizo2022 ~ microsite, data = microsite_obvs_carrizo2022)
##
## $coefficients
## total Ammospermophilus nelsoni Canis latrans
## (Intercept) 192.145833 7.3680556 1.420139
## microsite1 -59.770833 3.8819444 -1.420139
## microsite2 67.520833 -7.3680556 2.135417
## microsite3 -8.479167 -0.1458333 -1.420139
## Dipodomys heermanni Dipodomys ingens Dipodomys nelsoni
## (Intercept) 24.89583 10.302083 0.02777778
## microsite1 -24.89583 22.572917 -0.02777778
## microsite2 13.65972 -7.968750 -0.02777778
## microsite3 21.88194 -4.302083 0.08333333
## Eremophila alpestris Lepus californicus Sylvilagus audubonii
## (Intercept) 0.02777778 2.1076389 0.31597222
## microsite1 -0.02777778 1.5173611 0.05902778
## microsite2 -0.02777778 0.6701389 0.46180556
## microsite3 0.08333333 -1.3298611 -0.20486111
## Taxidea taxus Vulpes macrotis Xerospermophilus tereticaudus
## (Intercept) 0.09375 1.5069444 0.05902778
## microsite1 0.03125 -0.2569444 0.06597222
## microsite2 -0.09375 -0.2847222 0.05208333
## microsite3 -0.09375 2.0486111 -0.05902778
##
## $coef.sites
## 1 2 3 4 5
## (Intercept) 0.82266066 0.7511841735 0.60392815 0.603227854 0.60062292
## microsite1 -0.06453666 -0.0273964469 0.07373636 0.055148666 0.04437677
## microsite2 0.01845058 -0.0004842814 -0.05799813 -0.043338016 -0.02716873
## microsite3 0.03808407 0.0028563301 0.03187086 -0.005089014 0.01634156
## 6 7 8 9 10
## (Intercept) 0.66140967 0.65758160 0.75225703 0.881443013 0.79231121
## microsite1 -0.02392882 -0.04404994 0.06041279 0.003160946 -0.05420699
## microsite2 0.05327469 0.01947156 -0.07359020 0.049513677 0.07450031
## microsite3 0.01892447 0.01800510 0.01858545 -0.074476372 0.06120908
## 11 12 13 14 15
## (Intercept) 0.757975150 0.60232416 0.601659065 0.59665241 0.63171376
## microsite1 -0.033196501 0.07281243 0.054324307 0.04656352 -0.01179151
## microsite2 -0.009136013 -0.05835888 -0.043258895 -0.02730770 0.04341534
## microsite3 0.010708113 0.03059020 -0.003042873 0.01337918 0.02154298
## 16 17 18 19 20
## (Intercept) 0.64758896 0.74841897 0.82266066 0.79231121 0.77599521
## microsite1 -0.03295851 0.05786472 -0.06453666 -0.05420699 -0.01964839
## microsite2 0.01345977 -0.06858107 0.01845058 0.07450031 -0.03051729
## microsite3 0.02106733 0.02010260 0.03808407 0.06120908 0.01589608
## 21 22 23 24 25
## (Intercept) 0.61419784 0.61796585 0.62332179 0.64882697 0.67872759
## microsite1 0.07536999 0.05605755 0.02917215 -0.01702150 -0.06828895
## microsite2 -0.05902165 -0.03680169 -0.03196000 0.05291957 0.02593370
## microsite3 0.03524877 -0.01663815 0.02444723 0.01696852 0.02572792
## 26 27 28 29 30
## (Intercept) 0.75507939 0.881443013 0.79231121 0.64889133 0.60904276
## microsite1 0.06114378 0.003160946 -0.05420699 0.07223656 0.05626707
## microsite2 -0.07428727 0.049513677 0.07450031 -0.06930750 -0.03974522
## microsite3 0.01701317 -0.074476372 0.06120908 0.03718610 -0.01231795
## 31 32 33 34
## (Intercept) 0.62978120 0.64969741 0.68584857 0.7808930547
## microsite1 0.02439209 -0.01741272 -0.07093279 0.0612287151
## microsite2 -0.02973382 0.05313595 0.02637785 -0.0629854316
## microsite3 0.02527089 0.01731608 0.02652628 -0.0006302245
##
## $f.perms
## [,1]
## [1,] 1.0773695
## [2,] 1.4346662
## [3,] 0.7594084
## [4,] 1.1601047
## [5,] 0.8139389
## [6,] 1.8038832
## [7,] 1.0589874
## [8,] 1.0809882
## [9,] 0.9817717
## [10,] 0.5922997
## [11,] 0.5875530
## [12,] 1.6212800
## [13,] 0.4132547
## [14,] 0.9439013
## [15,] 0.8306422
## [16,] 1.2695341
## [17,] 1.3394904
## [18,] 2.2313127
## [19,] 1.4602037
## [20,] 0.8874217
## [21,] 0.8477149
## [22,] 1.6515857
## [23,] 0.9209062
## [24,] 0.8712447
## [25,] 1.8537751
## [26,] 0.4520303
## [27,] 0.6422675
## [28,] 0.6937360
## [29,] 0.5121132
## [30,] 1.0840413
## [31,] 0.7657220
## [32,] 0.7407049
## [33,] 0.7677447
## [34,] 0.6901325
## [35,] 0.2217002
## [36,] 1.1622272
## [37,] 0.9828825
## [38,] 0.4115368
## [39,] 0.9933325
## [40,] 0.7746668
## [41,] 0.3400434
## [42,] 1.3607540
## [43,] 0.4472123
## [44,] 1.6264029
## [45,] 0.7402980
## [46,] 1.3549384
## [47,] 0.9120707
## [48,] 1.7390446
## [49,] 0.7622935
## [50,] 0.4642489
## [51,] 0.8490542
## [52,] 1.1438176
## [53,] 1.2062347
## [54,] 0.8989609
## [55,] 0.2607377
## [56,] 0.8648469
## [57,] 2.3619086
## [58,] 0.8546718
## [59,] 1.5722175
## [60,] 0.5108489
## [61,] 1.3309682
## [62,] 0.6979797
## [63,] 0.9862426
## [64,] 1.8253666
## [65,] 0.7039515
## [66,] 2.4290442
## [67,] 1.0625229
## [68,] 0.8640515
## [69,] 1.0907585
## [70,] 1.1749779
## [71,] 1.6721614
## [72,] 0.9708889
## [73,] 0.5143696
## [74,] 1.1078374
## [75,] 0.5750246
## [76,] 1.1329288
## [77,] 1.1576832
## [78,] 1.2298644
## [79,] 1.0169410
## [80,] 0.4011755
## [81,] 1.0734196
## [82,] 0.4977247
## [83,] 1.9842955
## [84,] 0.5237694
## [85,] 0.4788469
## [86,] 0.5318913
## [87,] 0.9376171
## [88,] 1.0651569
## [89,] 1.3502565
## [90,] 0.5894219
## [91,] 0.8905804
## [92,] 2.0533064
## [93,] 0.9410248
## [94,] 0.5897965
## [95,] 1.0501578
## [96,] 1.3987373
## [97,] 0.9855457
## [98,] 1.1086486
## [99,] 0.5013632
## [100,] 0.5446618
## [101,] 0.5710572
## [102,] 0.8241510
## [103,] 1.9371325
## [104,] 0.8340133
## [105,] 0.6581928
## [106,] 1.1945682
## [107,] 0.8162550
## [108,] 0.9284143
## [109,] 1.3671057
## [110,] 0.5741838
## [111,] 0.8438246
## [112,] 1.8252784
## [113,] 0.8199549
## [114,] 0.9219512
## [115,] 0.9635522
## [116,] 1.0228218
## [117,] 1.0043853
## [118,] 0.5581192
## [119,] 1.2178961
## [120,] 1.7275631
## [121,] 0.5337335
## [122,] 1.1932273
## [123,] 0.7436823
## [124,] 0.3348509
## [125,] 1.3205523
## [126,] 1.5482372
## [127,] 0.4907302
## [128,] 0.9291597
## [129,] 0.8111345
## [130,] 0.8999173
## [131,] 0.7730638
## [132,] 0.6917351
## [133,] 0.9359345
## [134,] 1.3238360
## [135,] 0.9405476
## [136,] 1.2037680
## [137,] 0.6459283
## [138,] 0.6917776
## [139,] 1.8892990
## [140,] 1.6743325
## [141,] 0.5112099
## [142,] 1.3450152
## [143,] 0.7823561
## [144,] 0.7483766
## [145,] 0.4285560
## [146,] 0.6800580
## [147,] 0.7163132
## [148,] 3.3104624
## [149,] 0.6794660
## [150,] 1.0430674
## [151,] 1.4759836
## [152,] 0.8442450
## [153,] 0.7198996
## [154,] 1.3142312
## [155,] 1.7408914
## [156,] 1.2148176
## [157,] 0.9990606
## [158,] 0.6137924
## [159,] 1.3734379
## [160,] 1.2646388
## [161,] 1.4897055
## [162,] 0.8499780
## [163,] 2.6170316
## [164,] 0.6517809
## [165,] 0.9256920
## [166,] 1.0475426
## [167,] 1.6999764
## [168,] 0.9324713
## [169,] 1.6867151
## [170,] 0.3982554
## [171,] 1.3206805
## [172,] 1.3555704
## [173,] 0.3887953
## [174,] 0.4624891
## [175,] 0.4486391
## [176,] 1.3802087
## [177,] 1.8042189
## [178,] 1.2726028
## [179,] 0.5112027
## [180,] 0.8814058
## [181,] 0.4707463
## [182,] 1.3062912
## [183,] 0.5901703
## [184,] 0.7645781
## [185,] 0.6730904
## [186,] 0.5202965
## [187,] 1.1969601
## [188,] 1.7979016
## [189,] 0.8290130
## [190,] 0.5345689
## [191,] 0.9205430
## [192,] 2.5646463
## [193,] 0.9192182
## [194,] 1.0023936
## [195,] 1.3263514
## [196,] 0.7593473
## [197,] 1.3850690
## [198,] 0.9044589
## [199,] 0.4761108
## [200,] 1.7232842
## [201,] 1.4049950
## [202,] 1.5809669
## [203,] 1.1560883
## [204,] 1.3779510
## [205,] 0.8539367
## [206,] 0.4735716
## [207,] 0.3525171
## [208,] 2.3191742
## [209,] 0.8168767
## [210,] 0.8504346
## [211,] 0.8420970
## [212,] 1.7265849
## [213,] 1.2582063
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## [220,] 0.6682102
## [221,] 1.4194424
## [222,] 1.1727070
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## [226,] 1.9334518
## [227,] 1.4422665
## [228,] 1.7354266
## [229,] 0.8257635
## [230,] 2.3925984
## [231,] 1.0694065
## [232,] 1.3108524
## [233,] 0.7150875
## [234,] 0.6283255
## [235,] 0.7143620
## [236,] 0.5379952
## [237,] 0.6901832
## [238,] 0.9484795
## [239,] 0.8492347
## [240,] 0.9017705
## [241,] 0.4464448
## [242,] 0.8939154
## [243,] 1.4376909
## [244,] 0.7134428
## [245,] 0.7742249
## [246,] 0.7828549
## [247,] 1.4874285
## [248,] 0.4781466
## [249,] 1.5274064
## [250,] 0.4649723
## [251,] 1.1349872
## [252,] 1.0911615
## [253,] 0.7319726
## [254,] 1.5135241
## [255,] 0.9325407
## [256,] 1.0507885
## [257,] 0.8223239
## [258,] 0.4047382
## [259,] 1.8642744
## [260,] 1.5694074
## [261,] 1.1601954
## [262,] 0.7317296
## [263,] 1.3987564
## [264,] 0.8231764
## [265,] 0.9367926
## [266,] 1.4238097
## [267,] 0.7063184
## [268,] 0.8527986
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## [938,] 0.6588200
## [939,] 0.5556803
## [940,] 0.4415477
## [941,] 1.1151609
## [942,] 0.6325285
## [943,] 1.2351915
## [944,] 0.9268118
## [945,] 0.6679873
## [946,] 0.7900747
## [947,] 1.6985406
## [948,] 0.5843778
## [949,] 0.7277993
## [950,] 1.8579563
## [951,] 1.5994954
## [952,] 1.3758466
## [953,] 0.8298203
## [954,] 0.8364771
## [955,] 1.8116026
## [956,] 0.9164043
## [957,] 1.1747654
## [958,] 0.5799742
## [959,] 1.0400606
## [960,] 0.5294579
## [961,] 0.8607796
## [962,] 0.6590123
## [963,] 0.9132961
## [964,] 0.8652726
## [965,] 0.9070996
## [966,] 0.6703851
## [967,] 0.5581117
## [968,] 0.9302870
## [969,] 0.9769511
## [970,] 1.2399394
## [971,] 1.3126874
## [972,] 0.5166795
## [973,] 0.8667119
## [974,] 0.8244645
## [975,] 1.2707546
## [976,] 0.4764990
## [977,] 0.9576390
## [978,] 0.6026171
## [979,] 0.4451594
## [980,] 0.7651295
## [981,] 0.6343524
## [982,] 1.0795462
## [983,] 0.6733967
## [984,] 1.2230154
## [985,] 0.6298828
## [986,] 1.4182619
## [987,] 1.1835089
## [988,] 1.1506621
## [989,] 1.2784158
## [990,] 0.5309505
## [991,] 1.7040299
## [992,] 1.3783083
## [993,] 0.6969415
## [994,] 0.5355270
## [995,] 0.6340151
## [996,] 0.8906703
## [997,] 0.4760332
## [998,] 1.4672961
## [999,] 1.7217013
##
## $model.matrix
## (Intercept) microsite1 microsite2 microsite3
## 1 1 1 0 0
## 2 1 0 0 1
## 3 1 -1 -1 -1
## 4 1 0 1 0
## 5 1 -1 -1 -1
## 6 1 1 0 0
## 7 1 0 0 1
## 8 1 0 1 0
## 9 1 0 0 1
## 10 1 -1 -1 -1
## 11 1 1 0 0
## 12 1 0 1 0
## 13 1 1 0 0
## 14 1 0 0 1
## 15 1 -1 -1 -1
## 16 1 0 1 0
## 17 1 -1 -1 -1
## 18 1 0 1 0
## 19 1 1 0 0
## 20 1 0 1 0
## 21 1 -1 -1 -1
## 22 1 0 0 1
## 23 1 0 1 0
## 24 1 0 0 1
## 25 1 1 0 0
## 26 1 0 1 0
## 27 1 0 0 1
## 28 1 -1 -1 -1
## 29 1 0 1 0
## 30 1 0 0 1
## 31 1 1 0 0
## 32 1 -1 -1 -1
## 33 1 1 0 0
## 34 1 0 0 1
##
## $terms
## pca_data_final_carrizo2022 ~ microsite
## attr(,"variables")
## list(pca_data_final_carrizo2022, microsite)
## attr(,"factors")
## microsite
## pca_data_final_carrizo2022 0
## microsite 1
## attr(,"term.labels")
## [1] "microsite"
## attr(,"order")
## [1] 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist_final_carrizo2022<-vegdist(pca_data_final_carrizo2022, species = "bray")
res_final_carrizo2022<-pcoa(dist_final_carrizo2022)
p01_carrizo2022 <- as.data.frame(res_final_carrizo2022$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>% bind_cols(microsite_obvs_carrizo2022)
ggplot() +
geom_point(data = p01_carrizo2022, aes(x = Axis.1, y = Axis.2, color = microsite))+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ labs(color = "Microsite") + ggtitle('Carrizo 2022')
model1<-betadisper(dist_final_carrizo2022, microsite_obvs_carrizo2022$microsite)
anova(model1)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 0.00897 0.0029912 0.1016 0.9585
## Residuals 30 0.88327 0.0294423TukeyHSD(model1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## open-Ephedra californica -0.027085276 -0.2537954 0.1996248 0.9879161
## square-Ephedra californica 0.006470569 -0.2202396 0.2331807 0.9998284
## triangle-Ephedra californica -0.030425515 -0.2637083 0.2028573 0.9844077
## square-open 0.033555845 -0.1863853 0.2534970 0.9755051
## triangle-open -0.003340240 -0.2300504 0.2233699 0.9999763
## triangle-square -0.036896085 -0.2636062 0.1898140 0.9705518permutest(model1, pairwise = TRUE)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 0.00897 0.0029912 0.1016 999 0.955
## Residuals 30 0.88327 0.0294423
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Ephedra californica open square triangle
## Ephedra californica 0.76200 0.92600 0.680
## open 0.74985 0.71000 0.969
## square 0.92799 0.71272 0.648
## triangle 0.68695 0.97229 0.66433boxplot(model1, xlab = "Microsite")
### PCOA Carrizo 2023
pca_data_final_carrizo2023 <- microsite_obvs_carrizo2023%>%
spread(scientific_name, captures) %>% dplyr::select(-microsite) %>%
replace(is.na(.),0) %>% ungroup
dist_final_carrizo2023<-vegdist(pca_data_final_carrizo2023, species = "bray")
res_final_carrizo2023<-pcoa(dist_final_carrizo2023)
###make the rows equal manually
microsite_obvs_carrizo2023_new <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/microsite_obvs_carrizo2023_new.csv")
p01_carrizo2023 <- as.data.frame(res_final_carrizo2023$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>% bind_cols(microsite_obvs_carrizo2023_new)
ggplot() +
geom_point(data = p01_carrizo2023, aes(x = Axis.1, y = Axis.2, color = microsite))+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ labs(color = "Microsite")+ ggtitle('Carrizo 2023')
model2<-betadisper(dist_final_carrizo2023, microsite_obvs_carrizo2023_new$microsite)
anova(model2)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 0.03357 0.011189 0.2708 0.8457
## Residuals 22 0.90887 0.041312TukeyHSD(model2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## open-Ephedra californica 0.11612610 -0.2481943 0.4804465 0.8125365
## square-Ephedra californica 0.05797616 -0.2957820 0.4117343 0.9679262
## triangle-Ephedra californica 0.07527587 -0.2638880 0.4144397 0.9257820
## square-open -0.05814995 -0.3721547 0.2558548 0.9548066
## triangle-open -0.04085024 -0.3383166 0.2566161 0.9806383
## triangle-square 0.01729971 -0.2671325 0.3017320 0.9982330permutest(model2, pairwise = TRUE)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 0.03357 0.011189 0.2708 999 0.834
## Residuals 22 0.90887 0.041312
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Ephedra californica open square triangle
## Ephedra californica 0.44600 0.72500 0.600
## open 0.44743 0.55000 0.615
## square 0.72438 0.55446 0.850
## triangle 0.59801 0.63328 0.85891boxplot(model2, xlab = "Microsite")
### PCOA Carrizo Winter 2023
pca_data_final_carrizo2023_winter <- microsite_obvs_carrizo2023_winter%>%
spread(scientific_name, captures) %>%
replace(is.na(.),0)%>% ungroup() %>% dplyr::select(-microsite)
dist_final_carrizo2023_winter<-vegdist(pca_data_final_carrizo2023_winter, species = "bray")
res_final_carrizo2023_winter<-pcoa(dist_final_carrizo2023_winter)
###make the rows equal manually
microsite_obvs_carrizo2023_winter_new <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/microsite_obvs_carrizo2023_winter_new.csv")
p01_carrizo2023_winter <- as.data.frame(res_final_carrizo2023_winter$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>% bind_cols(microsite_obvs_carrizo2023_winter_new)
ggplot() +
geom_point(data = p01_carrizo2023_winter, aes(x = Axis.1, y = Axis.2, color = microsite))+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ labs(color = "Microsite") + ggtitle('Carrizo Winter 2023')
model3<-betadisper(dist_final_carrizo2023_winter, microsite_obvs_carrizo2023_winter_new$microsite)
anova(model3)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.05652 0.028259 0.4183 0.6662
## Residuals 14 0.94590 0.067564TukeyHSD(model3)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## square-Ephedra californica -0.07814121 -0.4764919 0.3202095 0.8661054
## triangle-Ephedra californica -0.13728528 -0.5356360 0.2610654 0.6479043
## triangle-square -0.05914407 -0.4894120 0.3711238 0.9314833permutest(model3, pairwise = TRUE)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.05652 0.028259 0.4183 999 0.659
## Residuals 14 0.94590 0.067564
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Ephedra californica square triangle
## Ephedra californica 0.62100 0.373
## square 0.61666 0.726
## triangle 0.38332 0.73363boxplot(model3, xlab = "Microsite")
### PCOA mojave 2022
pca_data_final_mojave2022 <- microsite_obvs_mojave2022%>%
spread(scientific_name, captures) %>%
replace(is.na(.),0)%>% ungroup() %>% dplyr::select(-microsite)
adonis(pca_data_final_mojave2022 ~ microsite, data = microsite_obvs_mojave2022)## 'adonis' will be deprecated: use 'adonis2' instead## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 3 0.29696 0.098986 0.43984 0.15861 0.966
## Residuals 7 1.57534 0.225048 0.84139
## Total 10 1.87229 1.00000
##
## $call
## adonis(formula = pca_data_final_mojave2022 ~ microsite, data = microsite_obvs_mojave2022)
##
## $coefficients
## total Canis latrans Dipodomys heermanni Dipodomys ingens
## (Intercept) 6.875 1.0625 0.1875 0.0625
## microsite1 -1.125 -1.0625 0.0625 -0.0625
## microsite2 1.125 1.9375 -0.1875 -0.0625
## microsite3 -0.375 -1.0625 0.3125 -0.0625
## Dipsosaurus dorsalis Lepus californicus Xerospermophilus mohavensis
## (Intercept) 0.1875 0.5625 0.875
## microsite1 0.5625 0.6875 -0.875
## microsite2 -0.1875 -0.5625 -0.875
## microsite3 -0.1875 -0.5625 0.625
##
## $coef.sites
## 1 2 3 4 5
## (Intercept) 0.61544053 0.3635299 0.42164537 0.42431662 0.4079359
## microsite1 -0.14812027 0.1210750 -0.03225940 0.08878293 0.1239501
## microsite2 0.03161829 -0.3635299 -0.08831204 -0.06431662 -0.3246025
## microsite3 0.01233725 0.1736130 0.12002130 -0.04196367 0.1707678
## 6 7 8 9 10
## (Intercept) 0.58174444 0.38419524 0.75162546 0.58174444 0.40121366
## microsite1 -0.08095079 -0.03298954 0.01169872 -0.08095079 -0.04034409
## microsite2 0.13254127 -0.11146797 0.09452839 0.13254127 -0.09686583
## microsite3 -0.19939150 0.11294762 -0.01412546 -0.19939150 0.11929916
## 11
## (Intercept) 0.50286620
## microsite1 0.09651797
## microsite2 -0.03619953
## microsite3 -0.01801771
##
## $f.perms
## [,1]
## [1,] 1.4557543
## [2,] 1.1507690
## [3,] 1.1251731
## [4,] 1.1869726
## [5,] 0.6456375
## [6,] 2.4452936
## [7,] 0.6603086
## [8,] 0.7163970
## [9,] 1.0267207
## [10,] 1.7961119
## [11,] 0.6978174
## [12,] 0.7689626
## [13,] 0.8776466
## [14,] 1.8413046
## [15,] 1.4148311
## [16,] 0.3856174
## [17,] 0.5520107
## [18,] 1.0320766
## [19,] 0.4352292
## [20,] 1.1259391
## [21,] 1.4487245
## [22,] 1.7634503
## [23,] 1.3181841
## [24,] 2.1225054
## [25,] 1.0720690
## [26,] 1.1297754
## [27,] 2.6959305
## [28,] 1.0021047
## [29,] 1.1082860
## [30,] 0.5139262
## [31,] 1.3910860
## [32,] 1.2458158
## [33,] 1.1779923
## [34,] 2.0117686
## [35,] 1.0627403
## [36,] 0.4477914
## [37,] 0.6167657
## [38,] 1.0538753
## [39,] 1.7627918
## [40,] 1.4929038
## [41,] 0.4392645
## [42,] 0.4419441
## [43,] 1.7947268
## [44,] 0.9802531
## [45,] 1.9580587
## [46,] 0.5422499
## [47,] 0.7238218
## [48,] 0.6251604
## [49,] 0.9599506
## [50,] 1.0890959
## [51,] 0.8977738
## [52,] 0.6517479
## [53,] 0.5000277
## [54,] 2.0586540
## [55,] 0.6019733
## [56,] 0.8939387
## [57,] 0.6188341
## [58,] 0.6097839
## [59,] 1.1821997
## [60,] 1.6116298
## [61,] 1.9470301
## [62,] 0.5985862
## [63,] 0.8083587
## [64,] 0.4160078
## [65,] 0.3923106
## [66,] 0.7011125
## [67,] 1.0081121
## [68,] 0.9792171
## [69,] 0.5802778
## [70,] 0.8161072
## [71,] 0.8033641
## [72,] 0.3880910
## [73,] 0.5039963
## [74,] 1.0722733
## [75,] 1.4418813
## [76,] 1.0217706
## [77,] 0.9128302
## [78,] 2.7743839
## [79,] 0.9925403
## [80,] 1.0056221
## [81,] 0.7216168
## [82,] 1.2184529
## [83,] 0.5991929
## [84,] 0.8267818
## [85,] 0.9088564
## [86,] 0.8242381
## [87,] 1.9017712
## [88,] 1.3560859
## [89,] 0.9189775
## [90,] 0.6017514
## [91,] 0.5741843
## [92,] 0.7462111
## [93,] 3.7369094
## [94,] 0.9148272
## [95,] 1.1820690
## [96,] 1.6134653
## [97,] 1.2908535
## [98,] 0.3938920
## [99,] 0.3799215
## [100,] 0.8650270
## [101,] 0.7217141
## [102,] 1.8691146
## [103,] 1.2374450
## [104,] 1.2056367
## [105,] 1.2879408
## [106,] 0.8794901
## [107,] 0.4751718
## [108,] 0.9400737
## [109,] 1.0076553
## [110,] 0.5754079
## [111,] 1.6778001
## [112,] 1.6087430
## [113,] 0.3716049
## [114,] 0.9431907
## [115,] 1.8510725
## [116,] 1.4427923
## [117,] 1.2392908
## [118,] 0.4971485
## [119,] 0.6371984
## [120,] 2.9948392
## [121,] 0.6449920
## [122,] 1.1219551
## [123,] 0.8067584
## [124,] 0.7707678
## [125,] 0.5512480
## [126,] 2.0764079
## [127,] 0.3416967
## [128,] 0.6129197
## [129,] 1.2229790
## [130,] 0.7468677
## [131,] 0.8030276
## [132,] 0.5749282
## [133,] 0.4258266
## [134,] 1.1865351
## [135,] 1.7227026
## [136,] 1.2005200
## [137,] 0.6857437
## [138,] 1.7892324
## [139,] 0.7034600
## [140,] 0.5595084
## [141,] 1.5194473
## [142,] 0.5157535
## [143,] 0.4692241
## [144,] 0.9557664
## [145,] 0.8365479
## [146,] 0.9510342
## [147,] 1.2109811
## [148,] 0.5236267
## [149,] 0.5675355
## [150,] 0.8827700
## [151,] 2.0109618
## [152,] 1.1499443
## [153,] 0.9224585
## [154,] 0.4587918
## [155,] 1.3355776
## [156,] 0.4902302
## [157,] 0.5838084
## [158,] 1.8891173
## [159,] 1.0611417
## [160,] 0.6155962
## [161,] 1.8213434
## [162,] 0.5695568
## [163,] 0.6903056
## [164,] 0.7721283
## [165,] 0.4780732
## [166,] 0.4881843
## [167,] 1.6350720
## [168,] 1.3625971
## [169,] 0.5775343
## [170,] 0.8816022
## [171,] 1.0170230
## [172,] 1.2913446
## [173,] 0.6198291
## [174,] 0.5650040
## [175,] 1.2470691
## [176,] 1.0280991
## [177,] 1.1346671
## [178,] 0.6976528
## [179,] 1.5130980
## [180,] 0.5228482
## [181,] 0.6078489
## [182,] 2.1089719
## [183,] 2.1282312
## [184,] 1.1307845
## [185,] 0.5691924
## [186,] 1.8689347
## [187,] 0.7290320
## [188,] 0.5780788
## [189,] 0.8276405
## [190,] 1.7647098
## [191,] 1.1647292
## [192,] 0.8476850
## [193,] 1.4527052
## [194,] 1.0195068
## [195,] 2.4600375
## [196,] 0.8963988
## [197,] 0.5716511
## [198,] 1.0185908
## [199,] 0.9348778
## [200,] 0.6413137
## [201,] 0.6787884
## [202,] 0.6405156
## [203,] 1.1256806
## [204,] 0.9029695
## [205,] 0.7168524
## [206,] 1.2262673
## [207,] 1.2197768
## [208,] 0.4877215
## [209,] 0.9939874
## [210,] 0.6433829
## [211,] 1.1656934
## [212,] 0.4237141
## [213,] 1.0767347
## [214,] 1.9823754
## [215,] 1.1758231
## [216,] 0.5162815
## [217,] 0.9271359
## [218,] 0.7103043
## [219,] 1.3206510
## [220,] 1.2553552
## [221,] 0.4676795
## [222,] 0.6161444
## [223,] 3.1071742
## [224,] 1.1494484
## [225,] 0.5388758
## [226,] 0.9585442
## [227,] 1.1610988
## [228,] 0.5398934
## [229,] 1.8691146
## [230,] 1.8025356
## [231,] 0.6357329
## [232,] 0.9353835
## [233,] 0.8769554
## [234,] 0.9364997
## [235,] 1.2538858
## [236,] 0.8959310
## [237,] 1.0767347
## [238,] 0.6369161
## [239,] 0.6472790
## [240,] 0.7813201
## [241,] 0.8605537
## [242,] 0.7385504
## [243,] 1.1528771
## [244,] 0.8208571
## [245,] 0.7331260
## [246,] 1.5872343
## [247,] 1.3260979
## [248,] 1.0549966
## [249,] 1.3131835
## [250,] 0.9820807
## [251,] 0.7103515
## [252,] 0.9489052
## [253,] 0.5730595
## [254,] 2.6493926
## [255,] 1.7373591
## [256,] 1.0093452
## [257,] 0.4351915
## [258,] 1.4310155
## [259,] 0.6531743
## [260,] 0.5175997
## [261,] 1.0259335
## [262,] 0.6811674
## [263,] 1.7982926
## [264,] 1.2409266
## [265,] 1.3633596
## [266,] 0.6048327
## [267,] 1.1550930
## [268,] 1.4536000
## [269,] 1.1667567
## [270,] 0.9856886
## [271,] 0.7832837
## [272,] 0.5739431
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## [942,] 1.5069291
## [943,] 0.6628427
## [944,] 0.7083188
## [945,] 1.1401569
## [946,] 1.0384334
## [947,] 0.7297349
## [948,] 0.6931509
## [949,] 0.7533514
## [950,] 0.9128302
## [951,] 0.9969548
## [952,] 2.7213493
## [953,] 0.9071029
## [954,] 1.2313625
## [955,] 0.9198353
## [956,] 0.5912835
## [957,] 0.7360906
## [958,] 1.4407631
## [959,] 0.9254243
## [960,] 0.9633722
## [961,] 1.3854207
## [962,] 0.7017276
## [963,] 0.5591768
## [964,] 0.6459604
## [965,] 0.6259468
## [966,] 0.6289635
## [967,] 1.3720777
## [968,] 0.6824417
## [969,] 0.9281690
## [970,] 0.8626836
## [971,] 0.6949957
## [972,] 0.8526568
## [973,] 0.4477914
## [974,] 0.5078317
## [975,] 0.6689640
## [976,] 1.4740143
## [977,] 0.6603180
## [978,] 1.3984599
## [979,] 5.4136160
## [980,] 1.9642518
## [981,] 1.2470691
## [982,] 0.6965158
## [983,] 0.8184531
## [984,] 0.8680996
## [985,] 4.5652299
## [986,] 0.9936159
## [987,] 0.4888627
## [988,] 0.6741451
## [989,] 1.0252034
## [990,] 0.8084566
## [991,] 0.5665792
## [992,] 1.0332217
## [993,] 1.0913569
## [994,] 0.7890015
## [995,] 0.9004815
## [996,] 2.2217508
## [997,] 1.6581510
## [998,] 1.0808812
## [999,] 1.3720777
##
## $model.matrix
## (Intercept) microsite1 microsite2 microsite3
## 1 1 1 0 0
## 2 1 0 1 0
## 3 1 -1 -1 -1
## 4 1 0 0 1
## 5 1 -1 -1 -1
## 6 1 1 0 0
## 7 1 1 0 0
## 8 1 -1 -1 -1
## 9 1 0 0 1
## 10 1 1 0 0
## 11 1 -1 -1 -1
##
## $terms
## pca_data_final_mojave2022 ~ microsite
## attr(,"variables")
## list(pca_data_final_mojave2022, microsite)
## attr(,"factors")
## microsite
## pca_data_final_mojave2022 0
## microsite 1
## attr(,"term.labels")
## [1] "microsite"
## attr(,"order")
## [1] 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"dist_final_mojave2022<-vegdist(pca_data_final_mojave2022, species = "bray")
res_final_mojave2022<-pcoa(dist_final_mojave2022)
p02_mojave2022 <- as.data.frame(res_final_mojave2022$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>% bind_cols(microsite_obvs_mojave2022)
ggplot() +
geom_point(data = p02_mojave2022, aes(x = Axis.1, y = Axis.2, color = microsite))+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ labs(color = "Microsite")+ ggtitle('Mojave 2022')
model4<-betadisper(dist_final_mojave2022, microsite_obvs_mojave2022$microsite)
anova(model4)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 0.12926 0.043086 0.5455 0.6667
## Residuals 7 0.55292 0.078988TukeyHSD(model4)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## open-Larrea tridentata -0.348935979 -1.3890607 0.6911887 0.6950734
## square-Larrea tridentata 0.033416962 -0.7722602 0.8390941 0.9989851
## triangle-Larrea tridentata 0.040545421 -0.6172872 0.6983781 0.9967097
## square-open 0.382352941 -0.7570466 1.5217525 0.6948943
## triangle-open 0.389481401 -0.6506433 1.4296061 0.6240468
## triangle-square 0.007128459 -0.7985487 0.8128056 0.9999901permutest(model4, pairwise = TRUE)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 0.12926 0.043086 0.5455 999 0.68
## Residuals 7 0.55292 0.078988
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Larrea tridentata open square triangle
## Larrea tridentata 0.89700 0.846
## open
## square 0.90506 0.966
## triangle 0.85641 0.97120boxplot(model4, xlab = "Microsite")
### PCOA mojave 2023
pca_data_final_mojave2023 <- microsite_obvs_mojave2023%>%
spread(scientific_name, captures) %>%
replace(is.na(.),0)%>% ungroup() %>% dplyr::select(-microsite)
dist_final_mojave2023<-vegdist(pca_data_final_mojave2023, species = "bray")
res_final_mojave2023<-pcoa(dist_final_mojave2023)
###make the rows equal manually
microsite_obvs_mojave2023_new <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/microsite_obvs_mojave2023_new.csv")
p02_mojave2023 <- as.data.frame(res_final_mojave2023$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>% bind_cols(microsite_obvs_mojave2023_new)
ggplot() +
geom_point(data = p02_mojave2023, aes(x = Axis.1, y = Axis.2, color = microsite))+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ labs(color = "Microsite")+ ggtitle('Mojave 2023')
model5<-betadisper(dist_final_mojave2023, microsite_obvs_mojave2023_new$microsite)## Warning in betadisper(dist_final_mojave2023,
## microsite_obvs_mojave2023_new$microsite): some squared distances are negative
## and changed to zeroanova(model5)## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 3 0.2221 0.074035 0.7226 0.554
## Residuals 15 1.5368 0.102450TukeyHSD(model5)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## open-Larrea tridentata -0.33237257 -0.9846880 0.3199428 0.4792273
## square-Larrea tridentata -0.10369488 -0.6991746 0.4917849 0.9573764
## triangle-Larrea tridentata -0.09283653 -0.6254498 0.4397768 0.9572625
## square-open 0.22867770 -0.4759035 0.9332589 0.7866207
## triangle-open 0.23953604 -0.4127794 0.8918514 0.7189084
## triangle-square 0.01085834 -0.5846214 0.6063381 0.9999454permutest(model5, pairwise = TRUE)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 0.2221 0.074035 0.7226 999 0.556
## Residuals 15 1.5368 0.102450
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Larrea tridentata open square triangle
## Larrea tridentata 0.030000 0.602000 0.564
## open 0.016972 0.452000 0.343
## square 0.619587 0.461186 0.954
## triangle 0.589579 0.338820 0.968655boxplot(model5, xlab = "Microsite")
### PCOA mojave winter 2023
pca_data_final_mojave2023_winter <- microsite_obvs_mojave2023_winter%>%
spread(scientific_name, captures) %>%
replace(is.na(.),0)%>% ungroup() %>% dplyr::select(-microsite)
dist_final_mojave2023_winter<-vegdist(pca_data_final_mojave2023_winter, species = "bray")
res_final_mojave2023_winter<-pcoa(dist_final_mojave2023_winter)
p02_mojave2023_winter <- as.data.frame(res_final_mojave2023_winter$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>% bind_cols(microsite_obvs_mojave2023_winter)
ggplot() +
geom_point(data = p02_mojave2023_winter, aes(x = Axis.1, y = Axis.2, color = microsite))+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ labs(color = "Microsite")+ ggtitle('Mojave Winter 2023')
model6<-betadisper(dist_final_mojave2023_winter, microsite_obvs_mojave2023_winter$microsite)
anova(model6)## Warning in anova.lm(lm(Distances ~ Groups, data = model.dat)): ANOVA F-tests on
## an essentially perfect fit are unreliable## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00074074 0.00074074 1.3954e+29 1.704e-15 ***
## Residuals 1 0.00000000 0.00000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(model6)## Warning in qtukey(conf.level, length(means), x$df.residual): NaNs produced## Warning in ptukey(abs(est), length(means), x$df.residual, lower.tail = FALSE):
## NaNs produced## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## square-Larrea tridentata 0.03333333 NaN NaN NaNpermutest(model6, pairwise = TRUE)## Warning in anova.lm(lm(Distances ~ Groups, data = model.dat)): ANOVA F-tests on
## an essentially perfect fit are unreliable## 'nperm' >= set of all permutations: complete enumeration.## Set of permutations < 'minperm'. Generating entire set.##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 5
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00074074 0.00074074 1.3954e+29 5 0.1667
## Residuals 1 0.00000000 0.00000000
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Larrea tridentata square
## Larrea tridentata
## squareboxplot(model5, xlab = "Microsite")
### Calculate abundance and richness for each site and year
library(stringr)
richness_carrizo_2022<-microsite_obvs_carrizo2022 %>% group_by(microsite)%>% summarise(animals = sum(captures), richness = n())
richness_carrizo_2023<-microsite_obvs_carrizo2023 %>% group_by(microsite)%>% summarise(animals = sum(captures), richness = n())
richness_mojave2022<-microsite_obvs_mojave2022 %>% group_by(microsite)%>% summarise(animals = sum(captures), richness = n())
richness_mojave2023<-microsite_obvs_mojave2023 %>% group_by(microsite)%>% summarise(animals = sum(captures), richness = n())
richness_carrizo2023_winter<-microsite_obvs_carrizo2023_winter%>% group_by(microsite)%>%summarise(animals = sum(captures), richness = n())
richness_mojave2023_winter<-microsite_obvs_mojave2023_winter%>% group_by(microsite)%>%summarise(animals = sum(captures), richness = n())
### Calculate abundance and richness for each site and year
evenness_carrizo2022 <- microsite_obvs_carrizo2022 %>%
group_by(microsite) %>%
summarize(evenness = diversity(captures, index = "shannon"))
evenness_carrizo2023 <- microsite_obvs_carrizo2023 %>%
group_by(microsite) %>%
summarize(evenness = diversity(captures, index = "shannon"))
evenness_mojave2022 <- microsite_obvs_mojave2022 %>%
group_by(microsite) %>%
summarize(evenness = diversity(captures, index = "shannon"))
evenness_mojave2023 <- microsite_obvs_mojave2023 %>%
group_by(microsite) %>%
summarize(evenness = diversity(captures, index = "shannon"))
evenness_carrizo2023_winter <- microsite_obvs_carrizo2023_winter %>%
group_by(microsite) %>%
summarize(evenness = diversity(captures, index = "shannon"))
evenness_mojave2023_winter <- microsite_obvs_mojave2023_winter%>%
group_by(microsite) %>%
summarize(evenness = diversity(captures, index = "shannon"))
data <-merge(evenness_mojave2023_winter, microsite_obvs_mojave2023_winter, by = "microsite")
data_1<-merge(data, richness_mojave2023_winter, by = "microsite")###join richness, abundance, and evenness data to temperature data
library(emmeans)
temp_mojave2023_winter <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/Mojave_winter.csv")
temp_mojave2023_winter_final <- inner_join(temp_mojave2023_winter, richness_mojave2023_winter, by = "microsite")
temp_mojave2023_winter_final <- inner_join(temp_mojave2023_winter_final, evenness_mojave2023_winter, by = "microsite")
A1<-ggplot(temp_mojave2023_winter_final, aes(x=richness, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+xlab("Richness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `show_guide` argument of `layer()` is deprecated as of ggplot2 2.0.0.
## ℹ Please use the `show.legend` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.glm_1.1<-glm(formula = richness ~ temp*as.factor(microsite), data = temp_mojave2023_winter_final)
anova(glm_1.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1042 229.17
## temp 1 0.00 1041 229.17 <2e-16 ***
## as.factor(microsite) 1 229.17 1040 0.00 <2e-16 ***
## temp:as.factor(microsite) 1 0.00 1039 0.00 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_1.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 1 1.04e-15 1039 1 1
## square 2 7.22e-16 1039 2 2
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -1 1.26e-15 1039 -790790212082967.000 <.0001B1<-ggplot(temp_mojave2023_winter_final, aes(x=animals, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+xlab("Abundance") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_1.2<-glm(formula = animals ~ temp*as.factor(microsite), data = temp_mojave2023_winter_final)
anova(glm_1.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1042 0.0000e+00
## temp 1 0.0000e+00 1041 2.3977e-24 1.0000000
## as.factor(microsite) 1 2.8229e-26 1040 2.3694e-24 0.0004882 ***
## temp:as.factor(microsite) 1 0.0000e+00 1039 2.4119e-24 1.0000000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_1.2, pairwise ~ as.factor(microsite)) ## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 3 2.61e-15 1039 3 3
## square 3 1.82e-15 1039 3 3
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -4.39e-15 3.18e-15 1039 -1.379 0.1683C1<-ggplot(temp_mojave2023_winter_final, aes(x=evenness, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+xlab("Evenness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_1.3<-glm(formula = evenness ~ temp*as.factor(microsite), data = temp_mojave2023_winter_final)
anova(glm_1.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1042 92.847
## temp 1 0.000 1041 92.847 <2e-16 ***
## as.factor(microsite) 1 92.847 1040 0.000 <2e-16 ***
## temp:as.factor(microsite) 1 0.000 1039 0.000 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_1.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.000 6.00e-16 1039 0.000 0.000
## square 0.637 4.17e-16 1039 0.637 0.637
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -0.637 7.31e-16 1039 -871241605796212.000 <.0001A1/B1/C1
temp_carrizo2023_winter <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/Carrizo_winter.csv")
temp_carrizo2023_winter_final <- inner_join(temp_carrizo2023_winter, richness_carrizo2023_winter, by = "microsite")
temp_carrizo2023_winter_final <- inner_join(temp_carrizo2023_winter_final, evenness_carrizo2023_winter, by = "microsite")
A2<-ggplot(temp_carrizo2023_winter_final, aes(x=richness, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Richness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_2.1<-glm(formula = richness ~ temp*as.factor(microsite), data = temp_carrizo2023_winter_final)
anova(glm_2.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 2019 1766.8
## temp 1 2.52 2018 1764.3 <2e-16 ***
## as.factor(microsite) 2 1764.26 2016 0.0 <2e-16 ***
## temp:as.factor(microsite) 2 0.00 2014 0.0 0.8939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_2.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 8 7.51e-16 2014 8 8
## square 5 3.88e-16 2014 5 5
## triangle 6 3.88e-16 2014 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - square 3 8.45e-16 2014 3548435964121619.000
## Ephedra californica - triangle 2 8.45e-16 2014 2365476818958502.000
## square - triangle -1 5.48e-16 2014 -1824387996209491.000
## p.value
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatesB2<-ggplot(temp_carrizo2023_winter_final, aes(x=animals, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Abundance") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_2.2<-glm(formula = animals ~ temp*as.factor(microsite), data = temp_carrizo2023_winter_final)
anova(glm_2.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 2019 1563902
## temp 1 3266 2018 1560635 <2e-16 ***
## as.factor(microsite) 2 1560635 2016 0 <2e-16 ***
## temp:as.factor(microsite) 2 0 2014 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_2.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 103 1.91e-14 2014 103 103
## square 13 9.85e-15 2014 13 13
## triangle 24 9.85e-15 2014 24 24
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - square 90 2.15e-14 2014 4189747062723428.000
## Ephedra californica - triangle 79 2.15e-14 2014 3677438091485935.000
## square - triangle -11 1.39e-14 2014 -789840628212904.000
## p.value
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatesC2<-ggplot(temp_carrizo2023_winter_final, aes(x=evenness, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Evenness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_2.3<-glm(formula = evenness ~ temp*as.factor(microsite), data = temp_carrizo2023_winter_final)
anova(glm_2.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 2019 0.061377
## temp 1 0.000135 2018 0.061242 < 2.2e-16 ***
## as.factor(microsite) 2 0.061242 2016 0.000000 < 2.2e-16 ***
## temp:as.factor(microsite) 2 0.000000 2014 0.000000 NaN
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_2.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.34 0 2014 1.34 1.34
## square 1.33 0 2014 1.33 1.33
## triangle 1.33 0 2014 1.33 1.33
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square 0.01754 0 2014 Inf <.0001
## Ephedra californica - triangle 0.01635 0 2014 Inf <.0001
## square - triangle -0.00119 0 2014 -Inf <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatesA2/B2/C2
temp_carrizo2023 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/Carrizo_2023.csv")
temp_carrizo2023_final <- inner_join(temp_carrizo2023, richness_carrizo_2023, by = "microsite")
temp_carrizo2023_final <- inner_join(temp_carrizo2023_final, evenness_carrizo2023, by = "microsite")
A3<-ggplot(temp_carrizo2023_final, aes(x=richness, y=temp, fill = microsite)) +
geom_boxplot()+ xlab("Richness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_3.1<-glm(formula = richness ~ temp*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_3.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 117523
## temp 1 68 28583 117455 <2e-16 ***
## as.factor(microsite) 3 117455 28580 0 <2e-16 ***
## temp:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_3.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 4 3.61e-14 28577 4 4
## open 6 3.62e-14 28577 6 6
## square 7 2.39e-14 28577 7 7
## triangle 10 2.88e-14 28577 10 10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open -2 5.11e-14 28577 -39116221894656.000
## Ephedra californica - square -3 4.33e-14 28577 -69212371737248.000
## Ephedra californica - triangle -6 4.62e-14 28577 -129781738905984.000
## open - square -1 4.34e-14 28577 -23056040512286.000
## open - triangle -4 4.63e-14 28577 -86472529307324.000
## square - triangle -3 3.75e-14 28577 -80055956630215.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesB3<-ggplot(temp_carrizo2023_final, aes(x=animals, y=temp, fill = microsite)) +
geom_boxplot()+ xlab("Abundance") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_3.2<-glm(formula = animals ~ temp*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_3.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 223625068
## temp 1 282018 28583 223343051 <2e-16 ***
## as.factor(microsite) 3 223343051 28580 0 <2e-16 ***
## temp:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_3.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 36 8.83e-13 28577 36 36
## open 35 8.84e-13 28577 35 35
## square 228 5.85e-13 28577 228 228
## triangle 212 7.05e-13 28577 212 212
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 1 1.25e-12 28577 800213721293.000
## Ephedra californica - square -192 1.06e-12 28577 -181235301332426.000
## Ephedra californica - triangle -176 1.13e-12 28577 -155759338759681.000
## open - square -193 1.06e-12 28577 -182062760819628.000
## open - triangle -177 1.13e-12 28577 -156556291703959.000
## square - triangle 16 9.16e-13 28577 17469137693105.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesC3<-ggplot(temp_carrizo2023_final, aes(x=evenness, y=temp, fill = microsite)) +
geom_boxplot()+ xlab("Evenness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_3.3<-glm(formula = evenness ~ temp*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_3.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 246.36
## temp 1 0.001 28583 246.36 <2e-16 ***
## as.factor(microsite) 3 246.361 28580 0.00 <2e-16 ***
## temp:as.factor(microsite) 3 0.000 28577 0.00 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_3.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.35 1.30e-15 28577 1.35 1.35
## open 1.34 1.30e-15 28577 1.34 1.34
## square 1.26 8.58e-16 28577 1.26 1.26
## triangle 1.49 1.03e-15 28577 1.49 1.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 0.0187 1.83e-15 28577 10186437074429.000
## Ephedra californica - square 0.0992 1.55e-15 28577 63814239521076.000
## Ephedra californica - triangle -0.1332 1.66e-15 28577 -80380881588710.000
## open - square 0.0805 1.55e-15 28577 51765179306969.000
## open - triangle -0.1519 1.66e-15 28577 -91595059372065.000
## square - triangle -0.2324 1.34e-15 28577 -172978098917653.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesA3/B3/C3## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).
temp_carrizo2022 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/Carrizo_2022.csv")
temp_carrizo2022_final <- inner_join(temp_carrizo2022, richness_carrizo_2022, by = "microsite")
temp_carrizo2022_final <- inner_join(temp_carrizo2022_final, evenness_carrizo2022, by = "microsite")
A4<-ggplot(temp_carrizo2022_final, aes(x=richness, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Richness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_4.1<-glm(formula = richness ~ temp*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_4.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 117523
## temp 1 68 28583 117455 <2e-16 ***
## as.factor(microsite) 3 117455 28580 0 <2e-16 ***
## temp:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_4.1,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 4 3.61e-14 28577 4 4
## open 6 3.62e-14 28577 6 6
## square 7 2.39e-14 28577 7 7
## triangle 10 2.88e-14 28577 10 10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open -2 5.11e-14 28577 -39116221894656.000
## Ephedra californica - square -3 4.33e-14 28577 -69212371737248.000
## Ephedra californica - triangle -6 4.62e-14 28577 -129781738905984.000
## open - square -1 4.34e-14 28577 -23056040512286.000
## open - triangle -4 4.63e-14 28577 -86472529307324.000
## square - triangle -3 3.75e-14 28577 -80055956630215.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesB4<-ggplot(temp_carrizo2022_final, aes(x=animals, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Abundance") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_4.2<-glm(formula = animals ~ temp*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_4.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 223625068
## temp 1 282018 28583 223343051 <2e-16 ***
## as.factor(microsite) 3 223343051 28580 0 <2e-16 ***
## temp:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_4.2,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 36 8.83e-13 28577 36 36
## open 35 8.84e-13 28577 35 35
## square 228 5.85e-13 28577 228 228
## triangle 212 7.05e-13 28577 212 212
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 1 1.25e-12 28577 800213721293.000
## Ephedra californica - square -192 1.06e-12 28577 -181235301332426.000
## Ephedra californica - triangle -176 1.13e-12 28577 -155759338759681.000
## open - square -193 1.06e-12 28577 -182062760819628.000
## open - triangle -177 1.13e-12 28577 -156556291703959.000
## square - triangle 16 9.16e-13 28577 17469137693105.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesC4<-ggplot(temp_carrizo2022_final, aes(x=evenness, y=temp, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Evenness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_4.3<-glm(formula = evenness ~ temp*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_4.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 246.36
## temp 1 0.001 28583 246.36 <2e-16 ***
## as.factor(microsite) 3 246.361 28580 0.00 <2e-16 ***
## temp:as.factor(microsite) 3 0.000 28577 0.00 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_4.3,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.35 1.30e-15 28577 1.35 1.35
## open 1.34 1.30e-15 28577 1.34 1.34
## square 1.26 8.58e-16 28577 1.26 1.26
## triangle 1.49 1.03e-15 28577 1.49 1.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 0.0187 1.83e-15 28577 10186437074429.000
## Ephedra californica - square 0.0992 1.55e-15 28577 63814239521076.000
## Ephedra californica - triangle -0.1332 1.66e-15 28577 -80380881588710.000
## open - square 0.0805 1.55e-15 28577 51765179306969.000
## open - triangle -0.1519 1.66e-15 28577 -91595059372065.000
## square - triangle -0.2324 1.34e-15 28577 -172978098917653.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesA4/B4/C4## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).
temp_mojave2023 <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/Mojave_2023.csv")
temp_mojave2023_final <- inner_join(temp_mojave2023, richness_mojave2023, by = "microsite")
temp_mojave2023_final <- inner_join(temp_mojave2023_final, evenness_mojave2023, by = "microsite")
A5<-ggplot(temp_mojave2023_final, aes(x=richness, y=temp, fill = microsite)) + geom_boxplot()+ xlab("Richness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_5.1<-glm(formula = richness ~ temp*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_5.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 12027
## temp 1 604.3 26675 11422 <2e-16 ***
## as.factor(microsite) 3 11422.4 26672 0 <2e-16 ***
## temp:as.factor(microsite) 3 0.0 26669 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_5.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 6 9.73e-15 26669 6 6
## open 5 1.04e-14 26669 5 5
## square 4 1.90e-14 26669 4 4
## triangle 6 1.04e-14 26669 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 1 1.43e-14 26669 70066546269864.000
## Larrea tridentata - square 2 2.14e-14 26669 93592911720361.000
## Larrea tridentata - triangle 0 1.42e-14 26669 1.000
## open - square 1 2.17e-14 26669 46072540267475.000
## open - triangle -1 1.47e-14 26669 -67842498871048.000
## square - triangle -2 2.17e-14 26669 -92232101206075.000
## p.value
## <.0001
## <.0001
## 0.6182
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesB5<-ggplot(temp_mojave2023_final, aes(x=animals, y=temp, fill = microsite)) + geom_boxplot()+ xlab("Abundance") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_5.2<-glm(formula = animals ~ temp*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_5.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 16686006
## temp 1 97871 26675 16588135 <2e-16 ***
## as.factor(microsite) 3 16588135 26672 0 <2e-16 ***
## temp:as.factor(microsite) 3 0 26669 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_5.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 53 2.32e-13 26669 53 53
## open 32 2.49e-13 26669 32 32
## square 76 4.55e-13 26669 76 76
## triangle 96 2.48e-13 26669 96 96
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 21 3.41e-13 26669 61594819226064.000
## Larrea tridentata - square -23 5.10e-13 26669 -45056243317339.000
## Larrea tridentata - triangle -43 3.40e-13 26669 -126398712668319.000
## open - square -44 5.18e-13 26669 -84861169821908.000
## open - triangle -64 3.52e-13 26669 -181759027178838.000
## square - triangle -20 5.18e-13 26669 -38609687117316.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesC5<-ggplot(temp_mojave2023_final, aes(evenness, y=temp, fill = microsite)) + geom_boxplot()+ xlab("Evenness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") +stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_5.3<-glm(formula = evenness ~ temp*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_5.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 2804.8
## temp 1 34.0 26675 2770.8 < 2.2e-16 ***
## as.factor(microsite) 3 2770.8 26672 0.0 < 2.2e-16 ***
## temp:as.factor(microsite) 3 0.0 26669 0.0 1.771e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_5.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.741 1.77e-15 26669 0.741 0.741
## open 1.470 1.90e-15 26669 1.470 1.470
## square 0.550 3.46e-15 26669 0.550 0.550
## triangle 0.990 1.89e-15 26669 0.990 0.990
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open -0.730 2.60e-15 26669 -281010560197461.000
## Larrea tridentata - square 0.191 3.89e-15 26669 49055900052129.000
## Larrea tridentata - triangle -0.249 2.59e-15 26669 -96132127831242.000
## open - square 0.920 3.95e-15 26669 233076660025633.000
## open - triangle 0.481 2.68e-15 26669 179213272908776.000
## square - triangle -0.440 3.94e-15 26669 -111476256624597.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesA5/B5/C5## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).
temp_mojave2022<- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/Mojave_2022.csv")
temp_mojave2022_final <- inner_join(temp_mojave2022, richness_mojave2022, by = "microsite")
temp_mojave2022_final <- inner_join(temp_mojave2022_final, evenness_mojave2022, by = "microsite")
A6<-ggplot(temp_mojave2023_final, aes(x=richness, y=temp, fill = microsite)) + geom_boxplot()+ xlab("Richness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_6.1<-glm(formula = richness ~ temp*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_6.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 12027
## temp 1 604.3 26675 11422 <2e-16 ***
## as.factor(microsite) 3 11422.4 26672 0 <2e-16 ***
## temp:as.factor(microsite) 3 0.0 26669 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_6.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 6 9.73e-15 26669 6 6
## open 5 1.04e-14 26669 5 5
## square 4 1.90e-14 26669 4 4
## triangle 6 1.04e-14 26669 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 1 1.43e-14 26669 70066546269864.000
## Larrea tridentata - square 2 2.14e-14 26669 93592911720361.000
## Larrea tridentata - triangle 0 1.42e-14 26669 1.000
## open - square 1 2.17e-14 26669 46072540267475.000
## open - triangle -1 1.47e-14 26669 -67842498871048.000
## square - triangle -2 2.17e-14 26669 -92232101206075.000
## p.value
## <.0001
## <.0001
## 0.6182
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesB6<-ggplot(temp_mojave2023_final, aes(x=animals, y=temp, fill = microsite)) + geom_boxplot()+ xlab("Abundance") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_6.2<-glm(formula = animals ~ temp*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_6.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 16686006
## temp 1 97871 26675 16588135 <2e-16 ***
## as.factor(microsite) 3 16588135 26672 0 <2e-16 ***
## temp:as.factor(microsite) 3 0 26669 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_6.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 53 2.32e-13 26669 53 53
## open 32 2.49e-13 26669 32 32
## square 76 4.55e-13 26669 76 76
## triangle 96 2.48e-13 26669 96 96
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 21 3.41e-13 26669 61594819226064.000
## Larrea tridentata - square -23 5.10e-13 26669 -45056243317339.000
## Larrea tridentata - triangle -43 3.40e-13 26669 -126398712668319.000
## open - square -44 5.18e-13 26669 -84861169821908.000
## open - triangle -64 3.52e-13 26669 -181759027178838.000
## square - triangle -20 5.18e-13 26669 -38609687117316.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesC6<-ggplot(temp_mojave2023_final, aes(evenness, y=temp, fill = microsite)) + geom_boxplot()+ xlab("Evenness") +
ylab("Temperature (°C)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_6.3<-glm(formula = evenness ~ temp*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_6.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 2804.8
## temp 1 34.0 26675 2770.8 < 2.2e-16 ***
## as.factor(microsite) 3 2770.8 26672 0.0 < 2.2e-16 ***
## temp:as.factor(microsite) 3 0.0 26669 0.0 1.771e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_6.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.741 1.77e-15 26669 0.741 0.741
## open 1.470 1.90e-15 26669 1.470 1.470
## square 0.550 3.46e-15 26669 0.550 0.550
## triangle 0.990 1.89e-15 26669 0.990 0.990
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open -0.730 2.60e-15 26669 -281010560197461.000
## Larrea tridentata - square 0.191 3.89e-15 26669 49055900052129.000
## Larrea tridentata - triangle -0.249 2.59e-15 26669 -96132127831242.000
## open - square 0.920 3.95e-15 26669 233076660025633.000
## open - triangle 0.481 2.68e-15 26669 179213272908776.000
## square - triangle -0.440 3.94e-15 26669 -111476256624597.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesA6/B6/C6## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).
###now compare humidity to abundance, richness, and evenness data
###no humidity data for Mojave and Carrizo Winter 2023
###Carrizo 2023
D1<-ggplot(temp_carrizo2023_final, aes(x=richness, y=humidity, fill = microsite)) +
geom_boxplot()+ xlab("Richness") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_7.1<-glm(formula = richness ~ humidity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_7.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 24190 118416
## humidity 1 65 24189 118351 < 2.2e-16 ***
## as.factor(microsite) 3 118351 24186 0 < 2.2e-16 ***
## humidity:as.factor(microsite) 3 0 24183 0 3.511e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_7.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 4 4.43e-15 24183 4 4
## open 6 4.68e-15 24183 6 6
## square 7 3.78e-15 24183 7 7
## triangle 10 3.59e-15 24183 10 10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open -2 6.45e-15 24183 -310141679974640.000
## Ephedra californica - square -3 5.83e-15 24183 -514960960624190.000
## Ephedra californica - triangle -6 5.71e-15 24183 -1051406810990360.000
## open - square -1 6.02e-15 24183 -166222419696377.000
## open - triangle -4 5.90e-15 24183 -677870444027862.000
## square - triangle -3 5.21e-15 24183 -575508029041332.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesD2<-ggplot(temp_carrizo2023_final, aes(x=animals, y=humidity, fill = microsite)) +
geom_boxplot()+ xlab("Abundance") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_7.2<-glm(formula = animals ~ humidity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_7.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 24190 118416
## humidity 1 65 24189 118351 < 2.2e-16 ***
## as.factor(microsite) 3 118351 24186 0 < 2.2e-16 ***
## humidity:as.factor(microsite) 3 0 24183 0 3.511e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_7.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 36 1.09e-13 24183 36 36
## open 35 1.15e-13 24183 35 35
## square 228 9.29e-14 24183 228 228
## triangle 212 8.83e-14 24183 212 212
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 1 1.59e-13 24183 6305938161382.000
## Ephedra californica - square -192 1.43e-13 24183 -1340213068447762.000
## Ephedra californica - triangle -176 1.40e-13 24183 -1254156610645360.000
## open - square -193 1.48e-13 24183 -1304567266464558.000
## open - triangle -177 1.45e-13 24183 -1219774476977153.000
## square - triangle 16 1.28e-13 24183 124815834029916.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesD3<-ggplot(temp_carrizo2023_final, aes(x=evenness, y=humidity, fill = microsite)) +
geom_boxplot()+ xlab("Evenness") +
ylab("Humidity(%)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_7.3<-glm(formula = evenness ~ humidity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_7.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 24190 203.49
## humidity 1 0.007 24189 203.48 < 2e-16 ***
## as.factor(microsite) 3 203.481 24186 0.00 < 2e-16 ***
## humidity:as.factor(microsite) 3 0.000 24183 0.00 0.00223 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_7.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.35 8.32e-15 24183 1.35 1.35
## open 1.34 8.78e-15 24183 1.34 1.34
## square 1.26 7.08e-15 24183 1.26 1.26
## triangle 1.49 6.74e-15 24183 1.49 1.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 0.0187 1.21e-14 24183 1544138575133.000
## Ephedra californica - square 0.0992 1.09e-14 24183 9077555324693.000
## Ephedra californica - triangle -0.1332 1.07e-14 24183 -12450038490181.000
## open - square 0.0805 1.13e-14 24183 7135153620595.000
## open - triangle -0.1519 1.11e-14 24183 -13727811801274.000
## square - triangle -0.2324 9.77e-15 24183 -23774401186530.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesD1/D2/D3## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_summary()`).
E1<-ggplot(temp_carrizo2022_final, aes(x=richness, y=humidity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Richness") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) +stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_8.1<-glm(formula = richness ~ humidity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_8.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 24190 118416
## humidity 1 65 24189 118351 < 2.2e-16 ***
## as.factor(microsite) 3 118351 24186 0 < 2.2e-16 ***
## humidity:as.factor(microsite) 3 0 24183 0 3.511e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_8.1,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 4 4.43e-15 24183 4 4
## open 6 4.68e-15 24183 6 6
## square 7 3.78e-15 24183 7 7
## triangle 10 3.59e-15 24183 10 10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open -2 6.45e-15 24183 -310141679974640.000
## Ephedra californica - square -3 5.83e-15 24183 -514960960624190.000
## Ephedra californica - triangle -6 5.71e-15 24183 -1051406810990360.000
## open - square -1 6.02e-15 24183 -166222419696377.000
## open - triangle -4 5.90e-15 24183 -677870444027862.000
## square - triangle -3 5.21e-15 24183 -575508029041332.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesE2<-ggplot(temp_carrizo2022_final, aes(x=animals, y=humidity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Abundance") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_8.2<-glm(formula = animals ~ humidity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_8.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 24190 196958140
## humidity 1 330987 24189 196627153 <2e-16 ***
## as.factor(microsite) 3 196627153 24186 0 <2e-16 ***
## humidity:as.factor(microsite) 3 0 24183 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_8.2,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 36 1.09e-13 24183 36 36
## open 35 1.15e-13 24183 35 35
## square 228 9.29e-14 24183 228 228
## triangle 212 8.83e-14 24183 212 212
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 1 1.59e-13 24183 6305938161382.000
## Ephedra californica - square -192 1.43e-13 24183 -1340213068447762.000
## Ephedra californica - triangle -176 1.40e-13 24183 -1254156610645360.000
## open - square -193 1.48e-13 24183 -1304567266464558.000
## open - triangle -177 1.45e-13 24183 -1219774476977153.000
## square - triangle 16 1.28e-13 24183 124815834029916.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesE3<-ggplot(temp_carrizo2022_final, aes(x=evenness, y=humidity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Evenness") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_8.3<-glm(formula = evenness ~ humidity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_8.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 24190 203.49
## humidity 1 0.007 24189 203.48 < 2e-16 ***
## as.factor(microsite) 3 203.481 24186 0.00 < 2e-16 ***
## humidity:as.factor(microsite) 3 0.000 24183 0.00 0.00223 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_8.3,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.35 8.32e-15 24183 1.35 1.35
## open 1.34 8.78e-15 24183 1.34 1.34
## square 1.26 7.08e-15 24183 1.26 1.26
## triangle 1.49 6.74e-15 24183 1.49 1.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 0.0187 1.21e-14 24183 1544138575133.000
## Ephedra californica - square 0.0992 1.09e-14 24183 9077555324693.000
## Ephedra californica - triangle -0.1332 1.07e-14 24183 -12450038490181.000
## open - square 0.0805 1.13e-14 24183 7135153620595.000
## open - triangle -0.1519 1.11e-14 24183 -13727811801274.000
## square - triangle -0.2324 9.77e-15 24183 -23774401186530.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesE1/E2/E3## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_summary()`).
F1<-ggplot(temp_mojave2023_final, aes(x=richness, y=humidity, fill = microsite)) + geom_boxplot()+ xlab("Richness") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_9.1<-glm(formula = richness ~ humidity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_9.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 17456 11777
## humidity 1 7.1 17455 11770 < 2.2e-16 ***
## as.factor(microsite) 3 11769.7 17452 0 < 2.2e-16 ***
## humidity:as.factor(microsite) 3 0.0 17449 0 8.224e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_9.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 6 1.28e-14 17449 6 6
## open 5 1.17e-14 17449 5 5
## square 4 1.17e-14 17449 4 4
## triangle 6 9.53e-15 17449 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 1 1.73e-14 17449 57728527495214.000
## Larrea tridentata - square 2 1.73e-14 17449 115426741239377.000
## Larrea tridentata - triangle 0 1.60e-14 17449 1.000
## open - square 1 1.65e-14 17449 60532866685042.000
## open - triangle -1 1.51e-14 17449 -66349617340108.000
## square - triangle -2 1.51e-14 17449 -132653216587306.000
## p.value
## <.0001
## <.0001
## 0.7472
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesF2<-ggplot(temp_mojave2023_final, aes(x=animals, y=humidity, fill = microsite)) + geom_boxplot()+ xlab("Abundance") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_9.2<-glm(formula = animals ~ humidity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_9.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 17456 10965960
## humidity 1 1057 17455 10964903 < 2.2e-16 ***
## as.factor(microsite) 3 10964903 17452 0 < 2.2e-16 ***
## humidity:as.factor(microsite) 3 0 17449 0 0.005268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_9.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 53 2.43e-13 17449 53 53
## open 32 2.22e-13 17449 32 32
## square 76 2.22e-13 17449 76 76
## triangle 96 1.81e-13 17449 96 96
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 21 3.30e-13 17449 63731984836151.000
## Larrea tridentata - square -23 3.30e-13 17449 -69783370938917.000
## Larrea tridentata - triangle -43 3.03e-13 17449 -141708615273037.000
## open - square -44 3.14e-13 17449 -140020488176199.000
## open - triangle -64 2.87e-13 17449 -223236942634324.000
## square - triangle -20 2.87e-13 17449 -69737352321875.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesF3<-ggplot(temp_mojave2023_final, aes(evenness, y=humidity, fill = microsite)) + geom_boxplot()+ xlab("Evenness") +
ylab("Humidity (%)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_9.3<-glm(formula = evenness ~ humidity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_9.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 17456 1890.2
## humidity 1 0.15 17455 1890.1 <2e-16 ***
## as.factor(microsite) 3 1890.07 17452 0.0 <2e-16 ***
## humidity:as.factor(microsite) 3 0.00 17449 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_9.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.741 4.54e-16 17449 0.741 0.741
## open 1.470 4.14e-16 17449 1.470 1.470
## square 0.550 4.15e-16 17449 0.550 0.550
## triangle 0.990 3.38e-16 17449 0.990 0.990
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open -0.730 6.15e-16 17449 -1186726500699234.000
## Larrea tridentata - square 0.191 6.15e-16 17449 310100848951874.000
## Larrea tridentata - triangle -0.249 5.66e-16 17449 -439882717657416.000
## open - square 0.920 5.86e-16 17449 1569625696643090.000
## open - triangle 0.481 5.35e-16 17449 898369478040840.000
## square - triangle -0.440 5.35e-16 17449 -821800289558537.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesF1/F2/F3## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_summary()`).
###No logger humidity data for Mojave 2022 because loggers did not record.###let's test abundance, richness, and evenness against light intensity
G1<-ggplot(temp_mojave2023_winter_final, aes(x=richness, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+xlab("Richness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_10.1<-glm(formula = richness ~ intensity*as.factor(microsite), data = temp_mojave2023_winter_final)
anova(glm_10.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1042 229.17
## intensity 1 1.324 1041 227.84 <2e-16 ***
## as.factor(microsite) 1 227.842 1040 0.00 <2e-16 ***
## intensity:as.factor(microsite) 1 0.000 1039 0.00 0.1976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_10.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 1 1.09e-15 1039 1 1
## square 2 7.50e-16 1039 2 2
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -1 1.32e-15 1039 -756312420321572.000 <.0001G2<-ggplot(temp_mojave2023_winter_final, aes(x=animals, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+xlab("Abundance") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_10.2<-glm(formula = animals ~ intensity*as.factor(microsite), data = temp_mojave2023_winter_final)
anova(glm_10.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1042 0.0000e+00
## intensity 1 0.0000e+00 1041 2.3635e-24 1.00000
## as.factor(microsite) 1 0.0000e+00 1040 2.3870e-24 1.00000
## intensity:as.factor(microsite) 1 1.0982e-26 1039 2.3760e-24 0.02842 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_10.2, pairwise ~ as.factor(microsite)) ## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 3 2.62e-15 1039 3 3
## square 3 1.81e-15 1039 3 3
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -4.05e-15 3.18e-15 1039 -1.273 0.2033G3<-ggplot(temp_mojave2023_winter_final, aes(x=evenness, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+xlab("Evenness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_10.3<-glm(formula = evenness ~ intensity*as.factor(microsite), data = temp_mojave2023_winter_final)
anova(glm_10.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 1042 92.847
## intensity 1 0.536 1041 92.310 <2e-16 ***
## as.factor(microsite) 1 92.310 1040 0.000 <2e-16 ***
## intensity:as.factor(microsite) 1 0.000 1039 0.000 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_10.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.000 6.1e-16 1039 0.000 0.000
## square 0.637 4.2e-16 1039 0.637 0.637
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -0.637 7.4e-16 1039 -859715171204072.000 <.0001G1/G2/G3
H1<-ggplot(temp_carrizo2023_winter_final, aes(x=richness, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Richness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_11.1<-glm(formula = richness ~ intensity*as.factor(microsite), data = temp_carrizo2023_winter_final)
anova(glm_11.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 2019 1766.8
## intensity 1 36.46 2018 1730.3 <2e-16 ***
## as.factor(microsite) 2 1730.32 2016 0.0 <2e-16 ***
## intensity:as.factor(microsite) 2 0.00 2014 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_11.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 8 2.18e-15 2014 8 8
## square 5 8.11e-16 2014 5 5
## triangle 6 8.08e-16 2014 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - square 3 2.32e-15 2014 1290722542352364.000
## Ephedra californica - triangle 2 2.32e-15 2014 860972325142544.000
## square - triangle -1 1.14e-15 2014 -873448949532552.000
## p.value
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatesH2<-ggplot(temp_carrizo2023_winter_final, aes(x=animals, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Abundance") +
ylab("Radiation (lum/ft²)")+
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_11.2<-glm(formula = animals ~ intensity*as.factor(microsite), data = temp_carrizo2023_winter_final)
anova(glm_11.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 2019 1563902
## intensity 1 30286 2018 1533616 <2e-16 ***
## as.factor(microsite) 2 1533616 2016 0 <2e-16 ***
## intensity:as.factor(microsite) 2 0 2014 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_11.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 103 1.07e-13 2014 103 103
## square 13 4.00e-14 2014 13 13
## triangle 24 3.98e-14 2014 24 24
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - square 90 1.15e-13 2014 785516494706426.000
## Ephedra californica - triangle 79 1.15e-13 2014 689902067960552.000
## square - triangle -11 5.64e-14 2014 -194908765721400.000
## p.value
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatesH3<-ggplot(temp_carrizo2023_winter_final, aes(x=evenness, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Evenness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA, size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_11.3<-glm(formula = evenness ~ intensity*as.factor(microsite), data = temp_carrizo2023_winter_final)
anova(glm_11.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 2019 0.061377
## intensity 1 0.001127 2018 0.060250 <2e-16 ***
## as.factor(microsite) 2 0.060250 2016 0.000000 <2e-16 ***
## intensity:as.factor(microsite) 2 0.000000 2014 0.000000 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_11.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.34 1.33e-17 2014 1.34 1.34
## square 1.33 4.97e-18 2014 1.33 1.33
## triangle 1.33 4.95e-18 2014 1.33 1.33
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - square 0.01754 1.42e-17 2014 1231776973156428.000
## Ephedra californica - triangle 0.01635 1.42e-17 2014 1148954846152655.000
## square - triangle -0.00119 7.02e-18 2014 -169469684677137.000
## p.value
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimatesH1/H2/H3
I1<-ggplot(temp_carrizo2023_final, aes(x=richness, y=intensity, fill = microsite)) +
geom_boxplot()+ xlab("Richness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_12.1<-glm(formula = richness ~ intensity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_12.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 117523
## intensity 1 0 28583 117522 <2e-16 ***
## as.factor(microsite) 3 117522 28580 0 <2e-16 ***
## intensity:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_12.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 4 3.67e-14 28577 4 4
## open 6 3.64e-14 28577 6 6
## square 7 2.42e-14 28577 7 7
## triangle 10 2.92e-14 28577 10 10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open -2 5.17e-14 28577 -38654899326517.000
## Ephedra californica - square -3 4.40e-14 28577 -68170270047057.000
## Ephedra californica - triangle -6 4.69e-14 28577 -127931935651836.000
## open - square -1 4.38e-14 28577 -22847106362120.000
## open - triangle -4 4.67e-14 28577 -85696283577158.000
## square - triangle -3 3.79e-14 28577 -79102734320028.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesI2<-ggplot(temp_carrizo2023_final, aes(x=animals, y=intensity, fill = microsite)) +
geom_boxplot()+ xlab("Abundance") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_12.2<-glm(formula = animals ~ intensity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_12.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 223625068
## intensity 1 1283469 28583 222341599 <2e-16 ***
## as.factor(microsite) 3 222341599 28580 0 <2e-16 ***
## intensity:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_12.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 36 8.96e-13 28577 36 36
## open 35 8.89e-13 28577 35 35
## square 228 5.91e-13 28577 228 228
## triangle 212 7.11e-13 28577 212 212
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 1 1.26e-12 28577 792330183285.000
## Ephedra californica - square -192 1.07e-12 28577 -178857286614291.000
## Ephedra californica - triangle -176 1.14e-12 28577 -153840980049092.000
## open - square -193 1.07e-12 28577 -180767421020377.000
## open - triangle -177 1.14e-12 28577 -155455793787529.000
## square - triangle 16 9.25e-13 28577 17295052049429.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesI3<-ggplot(temp_carrizo2023_final, aes(x=evenness, y=intensity, fill = microsite)) +
geom_boxplot()+ xlab("Evenness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_12.3<-glm(formula = evenness ~ intensity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_12.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 246.36
## intensity 1 2.043 28583 244.32 < 2.2e-16 ***
## as.factor(microsite) 3 244.319 28580 0.00 < 2.2e-16 ***
## intensity:as.factor(microsite) 3 0.000 28577 0.00 0.002801 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_12.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.35 1.32e-15 28577 1.35 1.35
## open 1.34 1.31e-15 28577 1.34 1.34
## square 1.26 8.68e-16 28577 1.26 1.26
## triangle 1.49 1.04e-15 28577 1.49 1.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 0.0187 1.85e-15 28577 10074726208647.000
## Ephedra californica - square 0.0992 1.58e-15 28577 62906016017117.000
## Ephedra californica - triangle -0.1332 1.68e-15 28577 -79301508296744.000
## open - square 0.0805 1.57e-15 28577 51339011222917.000
## open - triangle -0.1519 1.67e-15 28577 -90848795737773.000
## square - triangle -0.2324 1.36e-15 28577 -171061495512515.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesI1/I2/I3## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).
J1<-ggplot(temp_carrizo2022_final, aes(x=richness, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Richness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Carrizo 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_13.1<-glm(formula = richness ~ intensity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_13.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 117523
## intensity 1 0 28583 117522 <2e-16 ***
## as.factor(microsite) 3 117522 28580 0 <2e-16 ***
## intensity:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_13.1,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 4 3.67e-14 28577 4 4
## open 6 3.64e-14 28577 6 6
## square 7 2.42e-14 28577 7 7
## triangle 10 2.92e-14 28577 10 10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open -2 5.17e-14 28577 -38654899326517.000
## Ephedra californica - square -3 4.40e-14 28577 -68170270047057.000
## Ephedra californica - triangle -6 4.69e-14 28577 -127931935651836.000
## open - square -1 4.38e-14 28577 -22847106362120.000
## open - triangle -4 4.67e-14 28577 -85696283577158.000
## square - triangle -3 3.79e-14 28577 -79102734320028.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesJ2<-ggplot(temp_carrizo2022_final, aes(x=animals, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Abundance") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_13.2<-glm(formula = animals ~ intensity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_13.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 223625068
## intensity 1 1283469 28583 222341599 <2e-16 ***
## as.factor(microsite) 3 222341599 28580 0 <2e-16 ***
## intensity:as.factor(microsite) 3 0 28577 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_13.2,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 36 8.96e-13 28577 36 36
## open 35 8.89e-13 28577 35 35
## square 228 5.91e-13 28577 228 228
## triangle 212 7.11e-13 28577 212 212
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 1 1.26e-12 28577 792330183285.000
## Ephedra californica - square -192 1.07e-12 28577 -178857286614291.000
## Ephedra californica - triangle -176 1.14e-12 28577 -153840980049092.000
## open - square -193 1.07e-12 28577 -180767421020377.000
## open - triangle -177 1.14e-12 28577 -155455793787529.000
## square - triangle 16 9.25e-13 28577 17295052049429.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesJ3<-ggplot(temp_carrizo2022_final, aes(x=evenness, y=intensity, fill = microsite)) +
geom_boxplot()+ theme_classic()+ xlab("Evenness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_13.3<-glm(formula = evenness ~ intensity*as.factor(microsite), data = temp_carrizo2023_final)
anova(glm_13.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 28584 246.36
## intensity 1 2.043 28583 244.32 < 2.2e-16 ***
## as.factor(microsite) 3 244.319 28580 0.00 < 2.2e-16 ***
## intensity:as.factor(microsite) 3 0.000 28577 0.00 0.002801 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_13.3,pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.35 1.32e-15 28577 1.35 1.35
## open 1.34 1.31e-15 28577 1.34 1.34
## square 1.26 8.68e-16 28577 1.26 1.26
## triangle 1.49 1.04e-15 28577 1.49 1.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Ephedra californica - open 0.0187 1.85e-15 28577 10074726208647.000
## Ephedra californica - square 0.0992 1.58e-15 28577 62906016017117.000
## Ephedra californica - triangle -0.1332 1.68e-15 28577 -79301508296744.000
## open - square 0.0805 1.57e-15 28577 51339011222917.000
## open - triangle -0.1519 1.67e-15 28577 -90848795737773.000
## square - triangle -0.2324 1.36e-15 28577 -171061495512515.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesJ1/J2/J3## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).
K1<-ggplot(temp_mojave2023_final, aes(x=richness, y=intensity, fill = microsite)) + geom_boxplot()+ xlab("Richness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_14.1<-glm(formula = richness ~ intensity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_14.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 12027
## intensity 1 78.9 26675 11948 < 2.2e-16 ***
## as.factor(microsite) 3 11947.8 26672 0 < 2.2e-16 ***
## intensity:as.factor(microsite) 3 0.0 26669 0 5.821e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_14.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 6 9.88e-15 26669 6 6
## open 5 1.08e-14 26669 5 5
## square 4 2.23e-14 26669 4 4
## triangle 6 1.08e-14 26669 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 1 1.46e-14 26669 68276363057903.000
## Larrea tridentata - square 2 2.44e-14 26669 82021098682619.000
## Larrea tridentata - triangle 0 1.46e-14 26669 1.000
## open - square 1 2.48e-14 26669 40361904900326.000
## open - triangle -1 1.53e-14 26669 -65481424159355.000
## square - triangle -2 2.48e-14 26669 -80760722849789.000
## p.value
## <.0001
## <.0001
## 0.8410
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesK2<-ggplot(temp_mojave2023_final, aes(x=animals, y=intensity, fill = microsite)) + geom_boxplot()+ xlab("Abundance") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_14.2<-glm(formula = animals ~ intensity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_14.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 16686006
## intensity 1 16539 26675 16669466 < 2.2e-16 ***
## as.factor(microsite) 3 16669466 26672 0 < 2.2e-16 ***
## intensity:as.factor(microsite) 3 0 26669 0 3.927e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_14.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 53 2.30e-13 26669 53 53
## open 32 2.52e-13 26669 32 32
## square 76 5.19e-13 26669 76 76
## triangle 96 2.51e-13 26669 96 96
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 21 3.41e-13 26669 61553590078932.000
## Larrea tridentata - square -23 5.68e-13 26669 -40493669781504.000
## Larrea tridentata - triangle -43 3.41e-13 26669 -126203436282411.000
## open - square -44 5.77e-13 26669 -76240905457029.000
## open - triangle -64 3.56e-13 26669 -179912693087451.000
## square - triangle -20 5.77e-13 26669 -34670803901002.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesK3<-ggplot(temp_mojave2023_final, aes(evenness, y=intensity, fill = microsite)) + geom_boxplot()+ xlab("Evenness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") +stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_14.3<-glm(formula = evenness ~ intensity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_14.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 2804.8
## intensity 1 5.99 26675 2798.8 <2e-16 ***
## as.factor(microsite) 3 2798.83 26672 0.0 <2e-16 ***
## intensity:as.factor(microsite) 3 0.00 26669 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_14.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.741 1.77e-15 26669 0.741 0.741
## open 1.470 1.93e-15 26669 1.470 1.470
## square 0.550 3.98e-15 26669 0.550 0.550
## triangle 0.990 1.93e-15 26669 0.990 0.990
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open -0.730 2.62e-15 26669 -278702292199344.000
## Larrea tridentata - square 0.191 4.36e-15 26669 43755444861400.000
## Larrea tridentata - triangle -0.249 2.61e-15 26669 -95258948081731.000
## open - square 0.920 4.43e-15 26669 207819604543198.000
## open - triangle 0.481 2.73e-15 26669 176053507957207.000
## square - triangle -0.440 4.43e-15 26669 -99347902609623.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesK1/K2/K3## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).
L1<-ggplot(temp_mojave2023_final, aes(x=richness, y=intensity, fill = microsite)) + geom_boxplot()+ xlab("Richness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ ggtitle('Mojave 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_15.1<-glm(formula = richness ~ intensity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_15.1, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 12027
## intensity 1 78.9 26675 11948 < 2.2e-16 ***
## as.factor(microsite) 3 11947.8 26672 0 < 2.2e-16 ***
## intensity:as.factor(microsite) 3 0.0 26669 0 5.821e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_15.1, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 6 9.88e-15 26669 6 6
## open 5 1.08e-14 26669 5 5
## square 4 2.23e-14 26669 4 4
## triangle 6 1.08e-14 26669 6 6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 1 1.46e-14 26669 68276363057903.000
## Larrea tridentata - square 2 2.44e-14 26669 82021098682619.000
## Larrea tridentata - triangle 0 1.46e-14 26669 1.000
## open - square 1 2.48e-14 26669 40361904900326.000
## open - triangle -1 1.53e-14 26669 -65481424159355.000
## square - triangle -2 2.48e-14 26669 -80760722849789.000
## p.value
## <.0001
## <.0001
## 0.8410
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesL2<-ggplot(temp_mojave2023_final, aes(x=animals, y=intensity, fill = microsite)) + geom_boxplot()+ xlab("Abundance") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_15.2<-glm(formula = animals ~ intensity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_15.2, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 16686006
## intensity 1 16539 26675 16669466 < 2.2e-16 ***
## as.factor(microsite) 3 16669466 26672 0 < 2.2e-16 ***
## intensity:as.factor(microsite) 3 0 26669 0 3.927e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_15.2, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 53 2.30e-13 26669 53 53
## open 32 2.52e-13 26669 32 32
## square 76 5.19e-13 26669 76 76
## triangle 96 2.51e-13 26669 96 96
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open 21 3.41e-13 26669 61553590078932.000
## Larrea tridentata - square -23 5.68e-13 26669 -40493669781504.000
## Larrea tridentata - triangle -43 3.41e-13 26669 -126203436282411.000
## open - square -44 5.77e-13 26669 -76240905457029.000
## open - triangle -64 3.56e-13 26669 -179912693087451.000
## square - triangle -20 5.77e-13 26669 -34670803901002.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesL3<-ggplot(temp_mojave2023_final, aes(evenness, y=intensity, fill = microsite)) + geom_boxplot()+ xlab("Evenness") +
ylab("Radiation (lum/ft²)") +
labs(fill = "Microsite") + theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme(legend.position = "none") + stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE)
glm_15.3<-glm(formula = evenness ~ intensity*as.factor(microsite), data = temp_mojave2023_final)
anova(glm_15.3, test = "Chisq")## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 26676 2804.8
## intensity 1 5.99 26675 2798.8 <2e-16 ***
## as.factor(microsite) 3 2798.83 26672 0.0 <2e-16 ***
## intensity:as.factor(microsite) 3 0.00 26669 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(glm_15.3, pairwise ~ as.factor(microsite))## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.741 1.77e-15 26669 0.741 0.741
## open 1.470 1.93e-15 26669 1.470 1.470
## square 0.550 3.98e-15 26669 0.550 0.550
## triangle 0.990 1.93e-15 26669 0.990 0.990
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## Larrea tridentata - open -0.730 2.62e-15 26669 -278702292199344.000
## Larrea tridentata - square 0.191 4.36e-15 26669 43755444861400.000
## Larrea tridentata - triangle -0.249 2.61e-15 26669 -95258948081731.000
## open - square 0.920 4.43e-15 26669 207819604543198.000
## open - triangle 0.481 2.73e-15 26669 176053507957207.000
## square - triangle -0.440 4.43e-15 26669 -99347902609623.000
## p.value
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
## <.0001
##
## P value adjustment: tukey method for comparing a family of 4 estimatesL1/L2/L3## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).
###explore the relationship between light and temperature to use temperature as a proxy for animal observations models
cor.test(temp_mojave2022_final$temp,temp_mojave2022_final$intensity, method = "kendall")#positively correlated##
## Kendall's rank correlation tau
##
## data: temp_mojave2022_final$temp and temp_mojave2022_final$intensity
## z = 83.161, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.5790353cor.test(temp_mojave2023_final$temp,temp_mojave2023_final$intensity, method = "kendall")#positively correlated ##
## Kendall's rank correlation tau
##
## data: temp_mojave2023_final$temp and temp_mojave2023_final$intensity
## z = 121.02, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.5238221cor.test(temp_carrizo2022_final$temp,temp_carrizo2022_final$intensity, method = "kendall")#positively correlated##
## Kendall's rank correlation tau
##
## data: temp_carrizo2022_final$temp and temp_carrizo2022_final$intensity
## z = 158.58, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.6296155cor.test(temp_carrizo2023_final$temp,temp_carrizo2023_final$intensity, method = "kendall")#positively correlated##
## Kendall's rank correlation tau
##
## data: temp_carrizo2023_final$temp and temp_carrizo2023_final$intensity
## z = 148.04, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.6177645cor.test(temp_mojave2023_winter_final$temp,temp_mojave2023_winter_final$intensity, method = "kendall")#positively correlated ##
## Kendall's rank correlation tau
##
## data: temp_mojave2023_winter_final$temp and temp_mojave2023_winter_final$intensity
## z = 19.177, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.4349724cor.test(temp_carrizo2023_winter_final$temp,temp_carrizo2023_winter_final$intensity, method = "kendall")#positively correlated ##
## Kendall's rank correlation tau
##
## data: temp_carrizo2023_winter_final$temp and temp_carrizo2023_winter_final$intensity
## z = 37.586, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.6078502###Thus, we will use temperature as a proxy for light intensity in models.
###SUPPLEMENTARY PLOT
###Temperature
library(ggpubr)##
## Attaching package: 'ggpubr'## The following object is masked from 'package:ape':
##
## rotateggqqplot(temp_carrizo2022_final$humidity)## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 31542 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 31542 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_carrizo2022_final$humidity)#right-skewed
ggqqplot(temp_carrizo2023_final$humidity)## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 28587 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 28587 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_carrizo2023_final$humidity)#right-skewed
ggqqplot(temp_carrizo2023_winter_final$temp)
hist(temp_carrizo2023_winter_final$temp)#right-skewed
ggqqplot(temp_mojave2022_final$temp)
hist(temp_mojave2022_final$temp)#right-skewed
ggqqplot(temp_mojave2023_final$temp)## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 17457 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_mojave2023_final$temp)#right-skewed
ggqqplot(temp_mojave2023_winter_final$temp)
hist(temp_mojave2023_winter_final$temp)#right-skewed
###Humidity
ggqqplot(temp_carrizo2022_final$temp)## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 1788 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_carrizo2022_final$temp)#right-skewed
ggqqplot(temp_carrizo2023_final$temp)## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 24193 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_carrizo2023_final$temp)#bimodal distribution 
ggqqplot(temp_mojave2023_final$humidity)## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 26677 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_mojave2023_final$humidity)#right-skewed
###light intensity
ggqqplot(temp_carrizo2022_final$intensity)## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 1788 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_carrizo2022_final$intensity)#right-skewed
ggqqplot(temp_carrizo2023_final$intensity)## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 24193 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_carrizo2023_final$intensity)#right-skewed
ggqqplot(temp_carrizo2023_winter_final$intensity)
hist(temp_carrizo2023_winter_final$intensity)#right-skewed
ggqqplot(temp_mojave2022_final$intensity)
hist(temp_mojave2022_final$intensity)#right-skewed
ggqqplot(temp_mojave2023_final$intensity)## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_qq()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
## Removed 17457 rows containing non-finite outside the scale range
## (`stat_qq_line()`).
hist(temp_mojave2023_final$intensity)#right-skewed
ggqqplot(temp_mojave2023_winter_final$intensity)
hist(temp_mojave2023_winter_final$intensity)#right-skewed
### RII Carrizo 2022
###temp
temp_carrizo2022_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_carrizo2022_wide.csv")
rii.temp.shrub.carrizo2022<- temp_carrizo2022_wide %>%
mutate(rii_calc_shrub = (temp.Ephedra.californica-temp.open)/(temp.Ephedra.californica + temp.open))
rii.temp.triangle.carrizo2022<- temp_carrizo2022_wide %>%
mutate(rii_calc_triangle = (temp.triangle-temp.open)/(temp.triangle + temp.open))
rii.temp.square.carrizo2022<- temp_carrizo2022_wide %>%
mutate(rii_calc_square = (temp.square-temp.open)/(temp.square+ temp.open))
x <- select(rii.temp.shrub.carrizo2022, rii_calc_shrub)
y <- select(rii.temp.triangle.carrizo2022, rii_calc_triangle)
z <- select(rii.temp.square.carrizo2022, rii_calc_square)
rii.final.temp.carrizo2022<-cbind(x, y, z)
write.csv(rii.final.temp.carrizo2022, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_carrizo2022.csv")
rii_carrizo2022_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_carrizo2022_manual.csv")
ggplot(rii_carrizo2022_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Temperature")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Carrizo 2022')+ labs(fill = "Microsite") + ylim(-0.4, 0.4)## Warning: Removed 835 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 835 rows containing non-finite outside the scale range
## (`stat_summary()`).
library(ggpubr)
ggline(rii_carrizo2022_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Temperature", xlab = "Microsite", title = "Carrizo 2022")## Warning: Removed 646 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_carrizo2022<- glm(rii~as.factor(microsite), data = rii_carrizo2022_manual, family="gaussian")
emmeans(lm_rii_carrizo2022, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 0.313 0.0893 218 0.137 0.4895
## square -0.183 0.1067 218 -0.393 0.0277
## triangle -0.447 0.0375 218 -0.521 -0.3729
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square 0.496 0.1392 218 3.565 0.0013
## Ephedra californica - triangle 0.760 0.0969 218 7.851 <.0001
## square - triangle 0.264 0.1131 218 2.336 0.0531
##
## P value adjustment: tukey method for comparing a family of 3 estimates###only shrub was cooler than the open in Carrizo in 2022
### RII Carrizo 2023
###temp
temp_carrizo2023_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_carrizo2023_wide.csv")
rii.temp.shrub.carrizo2023<- temp_carrizo2023_wide %>%
mutate(rii_calc_shrub = (temp.Ephedra.californica-temp.open)/(temp.Ephedra.californica + temp.open))
rii.temp.triangle.carrizo2023<- temp_carrizo2023_wide %>%
mutate(rii_calc_triangle = (temp.triangle-temp.open)/(temp.triangle + temp.open))
rii.temp.square.carrizo2023<- temp_carrizo2023_wide %>%
mutate(rii_calc_square = (temp.square-temp.open)/(temp.square+ temp.open))
x2<- select(rii.temp.shrub.carrizo2023, rii_calc_shrub)
y2<- select(rii.temp.triangle.carrizo2023, rii_calc_triangle)
z2<- select(rii.temp.square.carrizo2023, rii_calc_square)
rii.final.temp.carrizo2023<-cbind(x2, y2, z2)
write.csv(rii.final.temp.carrizo2023, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_carrizo2023.csv")
rii_carrizo2023_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_carrizo2023_manual.csv")
ggplot(rii_carrizo2023_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Temperature")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Carrizo 2023') + ylim(-1,1) + labs(fill = "Microsite") ## Warning: Removed 9236 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 9236 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_carrizo2023_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Temperature", xlab = "Microsite", title = "Carrizo 2023")## Warning: Removed 9201 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_carrizo2023<- glm(rii~as.factor(microsite), data = rii_carrizo2023_manual, family="gaussian")
emmeans(lm_rii_carrizo2023, pairwise~microsite)###only shrub was cooler than the open in Carrizo in 2023## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica -0.0537 0.0287 2058 -0.1100 0.00249
## square 0.0289 0.0282 2058 -0.0263 0.08417
## triangle 0.0335 0.0282 2058 -0.0218 0.08871
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square -0.08266 0.0402 2058 -2.056 0.0994
## Ephedra californica - triangle -0.08720 0.0402 2058 -2.169 0.0768
## square - triangle -0.00454 0.0398 2058 -0.114 0.9929
##
## P value adjustment: tukey method for comparing a family of 3 estimates### RII Mojave 2022
###temp
temp_mojave2022_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_mojave2022_wide.csv")
rii.temp.shrub.mojave2022<- temp_mojave2022_wide %>%
mutate(rii_calc_shrub = (temp.Larrea.tridentata-temp.open)/(temp.Larrea.tridentata + temp.open))
rii.temp.triangle.mojave2022<- temp_mojave2022_wide %>%
mutate(rii_calc_triangle = (temp.triangle-temp.open)/(temp.triangle + temp.open))
rii.temp.square.mojave2022<- temp_mojave2022_wide %>%
mutate(rii_calc_square = (temp.square-temp.open)/(temp.square+ temp.open))
x3<- select(rii.temp.shrub.mojave2022, rii_calc_shrub)
y3<- select(rii.temp.triangle.mojave2022, rii_calc_triangle)
z3<- select(rii.temp.square.mojave2022, rii_calc_square)
rii.final.temp.mojave2022<-cbind(x3, y3, z3)
write.csv(rii.final.temp.mojave2022, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_mojave2022.csv")
rii_mojave2022_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_mojave2022_manual.csv")
ggplot(rii_mojave2022_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Temperature")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Mojave 2022')+ labs(fill = "Microsite") 
ggline(rii_mojave2022_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Temperture", xlab = "Microsite", title = "Mojave 2022")
lm_rii_mojave2022<- glm(rii~as.factor(microsite), data = rii_mojave2022_manual, family="gaussian")
emmeans(lm_rii_mojave2022, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata -3.08e-02 0.0085 63 -0.0478 -0.013793
## square -1.61e-02 0.0085 63 -0.0331 0.000925
## triangle 1.05e-05 0.0085 63 -0.0170 0.017001
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -0.0147 0.012 63 -1.224 0.4436
## Larrea tridentata - triangle -0.0308 0.012 63 -2.561 0.0339
## square - triangle -0.0161 0.012 63 -1.337 0.3803
##
## P value adjustment: tukey method for comparing a family of 3 estimates###only shrub and square were cooler than the open
### RII mojave 2023
###temp
temp_mojave2023_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_mojave2023_wide.csv")
rii_carrizo2023_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_carrizo2023_manual.csv")
rii.temp.shrub.mojave2023<- temp_mojave2023_wide %>%
mutate(rii_calc_shrub = (temp.Larrea.tridentata-temp.open)/(temp.Larrea.tridentata + temp.open))
rii.temp.triangle.mojave2023<- temp_mojave2023_wide %>%
mutate(rii_calc_triangle = (temp.triangle-temp.open)/(temp.triangle + temp.open))
rii.temp.square.mojave2023<- temp_mojave2023_wide %>%
mutate(rii_calc_square = (temp.square-temp.open)/(temp.square+ temp.open))
x4<- select(rii.temp.shrub.mojave2023, rii_calc_shrub)
y4<- select(rii.temp.triangle.mojave2023, rii_calc_triangle)
z4<- select(rii.temp.square.mojave2023, rii_calc_square)
rii.final.temp.mojave2023<-cbind(x4, y4, z4)
rii_mojave2023_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_mojave2023_manual.csv")
ggplot(rii_mojave2023_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Temperature")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Mojave 2023') + ylim(-0.5,0.5) + labs(fill = "Microsite") ## Warning: Removed 6129 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 6129 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_mojave2023_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Temperature", xlab = "Microsite", title = "Mojave 2023")## Warning: Removed 6129 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_mojave2023<- glm(rii~as.factor(microsite), data = rii_mojave2023_manual, family="gaussian")
emmeans(lm_rii_mojave2023, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata -2.01e-03 0.00118 6231 -0.00433 0.000303
## square 2.35e-03 0.00272 6231 -0.00298 0.007688
## triangle 7.28e-05 0.00158 6231 -0.00302 0.003162
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -0.00436 0.00297 6231 -1.471 0.3051
## Larrea tridentata - triangle -0.00209 0.00197 6231 -1.059 0.5397
## square - triangle 0.00228 0.00315 6231 0.725 0.7489
##
## P value adjustment: tukey method for comparing a family of 3 estimates###let's try RII for radiation for carrizo 2022
intensity_carrizo2022_wide <- reshape(temp_carrizo2022, timevar = "microsite", v.names = "intensity", direction = "wide", idvar="hour_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (date,day,hour_OMEGA,day_humidity,temp,humidity,pendant_id,lat,long,region,site_code,sensor_pendant,rep)
## are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Ephedra californica: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenwrite.csv(intensity_carrizo2022_wide, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/radiation_carrizo2022_wide.csv")
intensity_carrizo2022_wide<- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/radiation_carrizo2022_wide.csv")
rii.intensity.shrub.carrizo2022<- intensity_carrizo2022_wide %>%
mutate(rii_calc_shrub = (intensity.Ephedra.californica-intensity.open)/(intensity.Ephedra.californica + intensity.open))
rii.intensity.triangle.carrizo2022<- intensity_carrizo2022_wide %>%
mutate(rii_calc_triangle = (intensity.triangle-intensity.open)/(intensity.triangle + intensity.open))
rii.intensity.square.carrizo2022<- intensity_carrizo2022_wide %>%
mutate(rii_calc_square= (intensity.square-intensity.open)/(intensity.square + intensity.open))
x7 <- select(rii.intensity.shrub.carrizo2022, rii_calc_shrub)
y7<- select(rii.intensity.triangle.carrizo2022, rii_calc_triangle)
z7<- select(rii.intensity.square.carrizo2022, rii_calc_square)
rii.final.intensity.carrizo2022<-cbind(x7, y7, z7)
###did not get any values for shrub or triangle
###does not work for humidity
write.csv(rii.final.intensity.carrizo2022, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_intensity_carrizo2022.csv")
rii_intensity_carrizo2022_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_carrizo2022_manual.csv")
ggplot(rii_intensity_carrizo2022_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Radiation")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Carrizo 2022') + labs(fill = "Microsite") ## Warning: Removed 646 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 646 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_intensity_carrizo2022_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Radiation", xlab = "Microsite", title = "Carrizo 2022")## Warning: Removed 646 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_intensity_carrizo2022<- glm(rii~as.factor(microsite), data = rii_intensity_carrizo2022_manual, family="gaussian")
emmeans(lm_rii_intensity_carrizo2022, pairwise~microsite)###facilitation and triangle were facilitative ## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 0.313 0.0893 218 0.137 0.4895
## square -0.183 0.1067 218 -0.393 0.0277
## triangle -0.447 0.0375 218 -0.521 -0.3729
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square 0.496 0.1392 218 3.565 0.0013
## Ephedra californica - triangle 0.760 0.0969 218 7.851 <.0001
## square - triangle 0.264 0.1131 218 2.336 0.0531
##
## P value adjustment: tukey method for comparing a family of 3 estimates### RII carrizo 2023
###humidity
humidity_carrizo2023_wide <- reshape(temp_carrizo2023, timevar = "microsite", v.names = "humidity", direction = "wide", idvar="hour_ELITECH")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,hourdate_HOBO,day_humidity,temp,intensity,pendant_id,lat,long,site_code,rep)
## are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Ephedra californica: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenhumidity_carrizo2023_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/humidity_carrizo2023_wide.csv")
rii.humidity.shrub.carrizo2023<- humidity_carrizo2023_wide %>%
mutate(rii_calc_shrub = (humidity.Ephedra.californica-humidity.open)/(humidity.Ephedra.californica + humidity.open))
rii.humidity.triangle.carrizo2023<- humidity_carrizo2023_wide %>%
mutate(rii_calc_triangle = (humidity.triangle-humidity.open)/(humidity.triangle + humidity.open))
rii.humidity.square.carrizo2023<- humidity_carrizo2023_wide %>%
mutate(rii_calc_square = (humidity.square-humidity.open)/(humidity.square + humidity.open))
x5 <- select(rii.humidity.shrub.carrizo2023, rii_calc_shrub)
y5<- select(rii.humidity.triangle.carrizo2023, rii_calc_triangle)
z5<- select(rii.humidity.square.carrizo2023, rii_calc_square)
rii.final.humidity.carrizo2023<-cbind(x5, y5, z5)
###did not get any values for shrub or triangle
###does not work for humidity
###let's try RII for radiation
intensity_carrizo2023_wide <- reshape(temp_carrizo2023, timevar = "microsite", v.names = "intensity", direction = "wide", idvar="hourdate_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,hour_ELITECH,day_humidity,temp,humidity,pendant_id,lat,long,site_code,rep)
## are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Ephedra californica: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenintensity_carrizo2023_wide<- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/radiation_carrizo2023_wide.csv")
rii.intensity.shrub.carrizo2023<- intensity_carrizo2023_wide %>%
mutate(rii_calc_shrub = (intensity.Ephedra.californica-intensity.open)/(intensity.Ephedra.californica + intensity.open))
rii.intensity.triangle.carrizo2023<- intensity_carrizo2023_wide %>%
mutate(rii_calc_triangle = (intensity.triangle-intensity.open)/(intensity.triangle + intensity.open))
rii.intensity.square.carrizo2023<- intensity_carrizo2023_wide %>%
mutate(rii_calc_square= (intensity.square-intensity.open)/(intensity.square + intensity.open))
x6 <- select(rii.intensity.shrub.carrizo2023, rii_calc_shrub)
y6<- select(rii.intensity.triangle.carrizo2023, rii_calc_triangle)
z6<- select(rii.intensity.square.carrizo2023, rii_calc_square)
rii.final.intensity.carrizo2023<-cbind(x6, y6, z6)
write.csv(rii.final.intensity.carrizo2023, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_intensity_carrizo2023.csv")
rii_intensity_carrizo2023_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_intensity_carrizo2023_manual.csv")
ggplot(rii_intensity_carrizo2023_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Radiation")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Carrizo 2023') + labs(fill = "Microsite") ## Warning: Removed 10015 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 10015 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_intensity_carrizo2023_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Radiation", xlab = "Microsite", title = "Carrizo 2023")## Warning: Removed 10015 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_intensity_carrizo2023<- glm(rii~as.factor(microsite), data = rii_intensity_carrizo2023_manual, family="gaussian")
emmeans(lm_rii_intensity_carrizo2023, pairwise~microsite)###all of them were not facilitative## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 0.202 0.0178 1244 0.167 0.237
## square 0.345 0.0174 1244 0.311 0.379
## triangle 0.317 0.0174 1244 0.283 0.351
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square -0.1433 0.0248 1244 -5.765 <.0001
## Ephedra californica - triangle -0.1150 0.0248 1244 -4.626 <.0001
## square - triangle 0.0283 0.0246 1244 1.153 0.4819
##
## P value adjustment: tukey method for comparing a family of 3 estimates###let's try RII for radiation for Mojave 2022
intensity_mojave2022_wide <- reshape(temp_mojave2022, timevar = "microsite", v.names = "intensity", direction = "wide", idvar="hour_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,date,temp,pendant_id,lat,long,site_code,rep) are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Larrea tridentata: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenwrite.csv(intensity_mojave2022_wide, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/radiation_mojave2022_wide.csv")
intensity_mojave2022_wide<- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/radiation_mojave2022_wide.csv")
rii.intensity.shrub.mojave2022<- intensity_mojave2022_wide %>%
mutate(rii_calc_shrub = (intensity.Larrea.tridentata-intensity.open)/(intensity.Larrea.tridentata + intensity.open))
rii.intensity.triangle.mojave2022<- intensity_mojave2022_wide %>%
mutate(rii_calc_triangle = (intensity.triangle-intensity.open)/(intensity.triangle + intensity.open))
rii.intensity.square.mojave2022<- intensity_mojave2022_wide %>%
mutate(rii_calc_square= (intensity.square-intensity.open)/(intensity.square + intensity.open))
x8 <- select(rii.intensity.shrub.mojave2022, rii_calc_shrub)
y8<- select(rii.intensity.triangle.mojave2022, rii_calc_triangle)
z8<- select(rii.intensity.square.mojave2022, rii_calc_square)
rii.final.intensity.mojave2022<-cbind(x8, y8, z8)
write.csv(rii.final.intensity.mojave2022, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_intensity_mojave2022.csv")
rii_intensity_mojave2022_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_mojave2022_manual.csv")
ggplot(rii_intensity_mojave2022_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Radiation")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Mojave 2022') + labs(fill = "Microsite") 
ggline(rii_intensity_mojave2022_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Radiation", xlab = "Microsite", title = "Mojave 2022")
lm_rii_intensity_mojave2022<- glm(rii~as.factor(microsite), data = rii_intensity_mojave2022_manual, family="gaussian")
emmeans(lm_rii_intensity_mojave2022, pairwise~microsite)###facilitation by larrea and square## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata -3.08e-02 0.0085 63 -0.0478 -0.013793
## square -1.61e-02 0.0085 63 -0.0331 0.000925
## triangle 1.05e-05 0.0085 63 -0.0170 0.017001
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -0.0147 0.012 63 -1.224 0.4436
## Larrea tridentata - triangle -0.0308 0.012 63 -2.561 0.0339
## square - triangle -0.0161 0.012 63 -1.337 0.3803
##
## P value adjustment: tukey method for comparing a family of 3 estimates###let's try RII for radiation for Mojave 2023
intensity_mojave2023_wide <- reshape(temp_mojave2023, timevar = "microsite", v.names = "intensity", direction = "wide", idvar="hourdate_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,hour_ELITECH,day_humidity,temp,humidity,pendant_id,lat,long,site_code,rep)
## are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Larrea tridentata: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenintensity_mojave2023_wide<- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/radiation_mojave2023_wide.csv")
rii.intensity.shrub.mojave2023<- intensity_mojave2023_wide %>%
mutate(rii_calc_shrub = (intensity.Larrea.tridentata-intensity.open)/(intensity.Larrea.tridentata + intensity.open))
rii.intensity.triangle.mojave2023<- intensity_mojave2023_wide %>%
mutate(rii_calc_triangle = (intensity.triangle-intensity.open)/(intensity.triangle + intensity.open))
rii.intensity.square.mojave2023<- intensity_mojave2023_wide %>%
mutate(rii_calc_square= (intensity.square-intensity.open)/(intensity.square + intensity.open))
x9 <- select(rii.intensity.shrub.mojave2023, rii_calc_shrub)
y9<- select(rii.intensity.triangle.mojave2023, rii_calc_triangle)
z9<- select(rii.intensity.square.mojave2023, rii_calc_square)
rii.final.intensity.mojave2023<-cbind(x9, y9, z9)
write.csv(rii.final.intensity.mojave2023, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_intensity_mojave2023.csv")
rii_intensity_mojave2023_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii_mojave2023_manual.csv")
ggplot(rii_intensity_mojave2023_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Radiation")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Mojave 2023') + labs(fill = "Microsite") ## Warning: Removed 6129 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 6129 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_intensity_mojave2023_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Radiation", xlab = "Microsite", title = "Mojave 2023")## Warning: Removed 6129 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_intensity_mojave2023<- glm(rii~as.factor(microsite), data = rii_intensity_mojave2023_manual, family="gaussian")
emmeans(lm_rii_intensity_mojave2023, pairwise~microsite)###facilitation by larrea and square## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata -2.01e-03 0.00118 6231 -0.00433 0.000303
## square 2.35e-03 0.00272 6231 -0.00298 0.007688
## triangle 7.28e-05 0.00158 6231 -0.00302 0.003162
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - square -0.00436 0.00297 6231 -1.471 0.3051
## Larrea tridentata - triangle -0.00209 0.00197 6231 -1.059 0.5397
## square - triangle 0.00228 0.00315 6231 0.725 0.7489
##
## P value adjustment: tukey method for comparing a family of 3 estimates### RII mojave winter 2023
###temp
temp_mojave2023winter_wide <- reshape(temp_mojave2023_winter, timevar = "microsite", v.names = "temp", direction = "wide", idvar="hour_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,date,intensity,pendant_id,lat,long,site_code,rep) are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Larrea tridentata: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenwrite.csv(temp_mojave2023winter_wide, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_mojave2023winter_wide.csv")
temp_mojave2023winter_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_mojave2023winter_wide.csv")
rii.temp.shrub.mojave2023winter<- temp_mojave2023winter_wide %>%
mutate(rii_calc_shrub = (temp.Larrea.tridentata-temp.open)/(temp.Larrea.tridentata + temp.open))
rii.temp.triangle.mojave2023winter<- temp_mojave2023winter_wide %>%
mutate(rii_calc_triangle = (temp.triangle-temp.open)/(temp.triangle + temp.open))
rii.temp.square.mojave2023winter<- temp_mojave2023winter_wide %>%
mutate(rii_calc_square = (temp.square-temp.open)/(temp.square+ temp.open))
x10<- select(rii.temp.shrub.mojave2023winter, rii_calc_shrub)
y10<- select(rii.temp.triangle.mojave2023winter, rii_calc_triangle)
z10<- select(rii.temp.square.mojave2023winter, rii_calc_square)
rii.final.temp.mojave2023winter<-cbind(x10, y10, z10)
write.csv(rii.final.temp.mojave2023winter, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.temp.mojave2023winter.csv")
rii_mojave2023winter_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.temp.mojave2023winter_manual.csv")
ggplot(rii_mojave2023winter_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Temperature")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Mojave Winter 2023') + labs(fill = "Microsite") 
ggline(rii_mojave2023winter_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Temperature", xlab = "Microsite", title = "Mojave Winter 2023")
lm_rii_mojave2023winter<- glm(rii~as.factor(microsite), data = rii_mojave2023winter_manual, family="gaussian")
emmeans(lm_rii_mojave2023winter, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larrea tridentata 0.0472 0.019 69 0.00926 0.0851
## squre 0.0221 0.019 69 -0.01584 0.0600
## triangle 0.0326 0.019 69 -0.00528 0.0705
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larrea tridentata - squre 0.0251 0.0269 69 0.934 0.6208
## Larrea tridentata - triangle 0.0145 0.0269 69 0.541 0.8514
## squre - triangle -0.0106 0.0269 69 -0.393 0.9185
##
## P value adjustment: tukey method for comparing a family of 3 estimates### RII mojave winter 2023
###intensity
intensity_mojave2023winter_wide <- reshape(temp_mojave2023_winter, timevar = "microsite", v.names = "intensity", direction = "wide", idvar="hour_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,date,temp,pendant_id,lat,long,site_code,rep) are really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Larrea tridentata: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenwrite.csv(intensity_mojave2023winter_wide, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/intensity_mojave2023winter_wide.csv")
intensity_mojave2023winter_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/intensity_mojave2023winter_wide.csv")
rii.intensity.shrub.mojave2023winter<- intensity_mojave2023winter_wide %>%
mutate(rii_calc_shrub = (intensity.Larrea.tridentata-intensity.open)/(intensity.Larrea.tridentata + intensity.open))
rii.intensity.triangle.mojave2023winter<- intensity_mojave2023winter_wide %>%
mutate(rii_calc_triangle = (intensity.triangle-intensity.open)/(intensity.triangle + intensity.open))
rii.intensity.square.mojave2023winter<- intensity_mojave2023winter_wide %>%
mutate(rii_calc_square = (intensity.square-intensity.open)/(intensity.square+ intensity.open))
x11<- select(rii.intensity.shrub.mojave2023winter, rii_calc_shrub)
y11<- select(rii.intensity.triangle.mojave2023winter, rii_calc_triangle)
z11<- select(rii.intensity.square.mojave2023winter, rii_calc_square)
rii.final.intensity.mojave2023winter<-cbind(x11, y11, z11)
write.csv(rii.final.intensity.mojave2023winter, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.intensity.mojave2023winter.csv")
rii_intensity_mojave2023winter_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.intensity.mojave2023winter_manual.csv")
ggplot(rii_intensity_mojave2023winter_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Radiation")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Mojave Winter 2023') + labs(fill = "Microsite") ## Warning: Removed 39 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 39 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_intensity_mojave2023winter_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Radiation", xlab = "Microsite", title = "Mojave Winter 2023")## Warning: Removed 39 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_intensity_mojave2023winter<- glm(rii~as.factor(microsite), data = rii_intensity_mojave2023winter_manual, family="gaussian")
emmeans(lm_rii_intensity_mojave2023winter, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Larre tridentata 0.0142 0.103 30 -0.196 0.225
## square 0.0224 0.103 30 -0.188 0.233
## triangle -0.3342 0.103 30 -0.545 -0.124
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Larre tridentata - square -0.0082 0.146 30 -0.056 0.9983
## Larre tridentata - triangle 0.3485 0.146 30 2.389 0.0590
## square - triangle 0.3567 0.146 30 2.445 0.0522
##
## P value adjustment: tukey method for comparing a family of 3 estimates### RII carrizo winter 2023
###temp
temp_carrizo2023winter_wide <- reshape(temp_carrizo2023_winter, timevar = "microsite", v.names = "temp", direction = "wide", idvar="hour_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,date,intensity,pendant_id,lat,long,site_code,sensor_pendant,rep) are
## really varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Ephedra californica: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenwrite.csv(temp_carrizo2023winter_wide, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_carrizo2023winter_wide.csv")
temp_carrizo2023winter_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/temp_carrizo2023winter_wide.csv")
rii.temp.shrub.carrizo2023winter<- temp_carrizo2023winter_wide %>%
mutate(rii_calc_shrub = (temp.Ephedra.californica-temp.open)/(temp.Ephedra.californica + temp.open))
rii.temp.triangle.carrizo2023winter<- temp_carrizo2023winter_wide %>%
mutate(rii_calc_triangle = (temp.triangle-temp.open)/(temp.triangle + temp.open))
rii.temp.square.carrizo2023winter<- temp_carrizo2023winter_wide %>%
mutate(rii_calc_square = (temp.square-temp.open)/(temp.square+ temp.open))
x12<- select(rii.temp.shrub.carrizo2023winter, rii_calc_shrub)
y12<- select(rii.temp.triangle.carrizo2023winter, rii_calc_triangle)
z12<- select(rii.temp.square.carrizo2023winter, rii_calc_square)
rii.final.temp.carrizo2023winter<-cbind(x12, y12, z12)
write.csv(rii.final.temp.carrizo2023winter, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.temp.carrizo2023winter.csv")
rii_carrizo2023winter_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.temp.carrizo2023winter_manual.csv")
ggplot(rii_carrizo2023winter_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Temperature")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Carrizo Winter 2023') + labs(fill = "Microsite") + ylim(-0.5,0.5)## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_carrizo2023winter_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Temperature", xlab = "Microsite", title = "Carrizo Winter 2023")
lm_rii_carrizo2023winter<- glm(rii~as.factor(microsite), data = rii_carrizo2023winter_manual, family="gaussian")
emmeans(lm_rii_carrizo2023winter, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica 1.958 1.28 69 -0.605 4.52
## square 0.999 1.28 69 -1.565 3.56
## triangle 0.555 1.28 69 -2.008 3.12
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square 0.960 1.82 69 0.528 0.8579
## Ephedra californica - triangle 1.403 1.82 69 0.772 0.7213
## square - triangle 0.444 1.82 69 0.244 0.9677
##
## P value adjustment: tukey method for comparing a family of 3 estimates### RII carrizo winter 2023
###intensity
intensity_carrizo2023winter_wide <- reshape(temp_carrizo2023_winter, timevar = "microsite", v.names = "intensity", direction = "wide", idvar="hour_HOBO")## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : some constant variables
## (day,date,temp,pendant_id,lat,long,site_code,sensor_pendant,rep) are really
## varying## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=square: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=triangle: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=Ephedra californica: first taken## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
## varying, : multiple rows match for microsite=open: first takenwrite.csv(intensity_carrizo2023winter_wide, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/intensity_carrizo2023winter_wide.csv")
intensity_carrizo2023winter_wide<-read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/intensity_carrizo2023winter_wide.csv")
rii.intensity.shrub.carrizo2023winter<- intensity_carrizo2023winter_wide %>%
mutate(rii_calc_shrub = (intensity.Ephedra.californica-intensity.open)/(intensity.Ephedra.californica + intensity.open))
rii.intensity.triangle.carrizo2023winter<- intensity_carrizo2023winter_wide %>%
mutate(rii_calc_triangle = (intensity.triangle-intensity.open)/(intensity.triangle + intensity.open))
rii.intensity.square.carrizo2023winter<- intensity_carrizo2023winter_wide %>%
mutate(rii_calc_square = (intensity.square-intensity.open)/(intensity.square+ intensity.open))
x13<- select(rii.intensity.shrub.carrizo2023winter, rii_calc_shrub)
y13<- select(rii.intensity.triangle.carrizo2023winter, rii_calc_triangle)
z13<- select(rii.intensity.square.carrizo2023winter, rii_calc_square)
rii.final.intensity.carrizo2023winter<-cbind(x13, y13, z13)
write.csv(rii.final.intensity.carrizo2023winter, "C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.intensity.carrizo2023winter.csv")
rii_intensity_carrizo2023winter_manual <- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/rii.final.intensity.carrizo2023winter_manual.csv")
ggplot(rii_intensity_carrizo2023winter_manual, aes(microsite, rii, fill = microsite)) +
geom_boxplot()+ ylab("Relative Interaction Index (RII) for Radiation")+ geom_hline(yintercept=0, linetype="dashed", color = "red")+ theme_classic() + xlab("Microsite")+ theme_classic()+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ stat_summary(fun.y=mean, colour="black", geom="point", shape=18, size=3,show_guide = FALSE) + ggtitle('Carrizo Winter 2023') + labs(fill = "Microsite") ## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggline(rii_intensity_carrizo2023winter_manual, x="microsite", y= "rii", add = "mean_se", ylab = "Relative Interaction Index (RII) for Radiation", xlab = "Microsite", title = "Carrizo Winter 2023")## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_summary()`).
lm_rii_intensity_carrizo2023winter<- glm(rii~as.factor(microsite), data = rii_intensity_carrizo2023winter_manual, family="gaussian")
emmeans(lm_rii_intensity_carrizo2023winter, pairwise~microsite)## $emmeans
## microsite emmean SE df lower.CL upper.CL
## Ephedra californica -0.637 0.136 33 -0.914 -0.3597
## square -0.373 0.136 33 -0.650 -0.0963
## triangle -0.500 0.136 33 -0.777 -0.2234
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Ephedra californica - square -0.263 0.193 33 -1.368 0.3689
## Ephedra californica - triangle -0.136 0.193 33 -0.708 0.7608
## square - triangle 0.127 0.193 33 0.661 0.7877
##
## P value adjustment: tukey method for comparing a family of 3 estimates###smooth plots for max temperature
ggplot(temp_carrizo2022, aes((day), temp, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Temperature (°C)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo 2022')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_carrizo2023, aes((day), temp, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Temperature (°C)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo 2023')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_carrizo2023_winter, aes((day), temp, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Temperature (°C)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo Winter 2023')## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(temp_mojave2022, aes((day), temp, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Temperature (°C)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave 2022')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
ggplot(temp_mojave2023, aes((day), temp, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Temperature (°C)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave 2023')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_mojave2023_winter, aes((day), temp, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Temperature (°C)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave Winter 2023')## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
###smooth plots for max light radiation
ggplot(temp_carrizo2022, aes((day), intensity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Radiation (lum/ft²)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo 2022')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_carrizo2023, aes((day), intensity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Radiation (lum/ft²)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo 2023')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_carrizo2023_winter, aes((day), intensity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Radiation (lum/ft²)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo Winter 2023')## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(temp_mojave2022, aes((day), intensity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Radiation (lum/ft²)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave 2022')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
ggplot(temp_mojave2023, aes((day), intensity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Radiation (lum/ft²)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave 2023')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_mojave2023_winter, aes((day), intensity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Radiation (lum/ft²)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave Winter 2023')## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
###smooth plots for max humidity. Really only got good humidity for Carrizo and Mojave spring/summer 2023.
ggplot(temp_carrizo2023, aes((day_humidity), humidity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Humidity (%)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Carrizo 2023') + xlim(0,30)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 28875 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 28875 rows containing non-finite outside the scale range
## (`stat_summary()`).
ggplot(temp_mojave2023, aes((day_humidity), humidity, color=microsite)) + geom_smooth()+ xlab("Day") + ylab ("Humidity (%)")+ theme_classic()+ theme(axis.text=element_text(size=12))+stat_summary(fun.y=max, geom="point", size=2, aes(shape = microsite))+ labs(color="Microsite", shape= "Microsite") + theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1))+ ggtitle('Mojave 2023')## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_summary()`).
###line graphs for temperature
ggline(temp_carrizo2022, x = "day", y = "temp", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Temperature (°C)", legend.title= "Microsite", legend="right")+ ggtitle('Carrizo 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0)))## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_carrizo2023, x = "day", y = "temp", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Temperature (°C)", legend.title= "Microsite", legend="right")+ ggtitle('Carrizo 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0)))## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_carrizo2023_winter, x = "day", y = "temp", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Temperature (°C)", legend.title= "Microsite", legend="right")+ ggtitle('Carrizo Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0)))
ggline(temp_mojave2022, x = "day", y = "temp", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Temperature (°C)", legend.title= "Microsite", legend="right")+ ggtitle('Mojave 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0)))
ggline(temp_mojave2023, x = "day", y = "temp", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Temperature (°C)", legend.title= "Microsite", legend="right")+ ggtitle('Mojave 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0)))## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_mojave2023_winter, x = "day", y = "temp", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Temperature (°C)", legend.title= "Microsite", legend="right")+ ggtitle('Mojave Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0)))
###line graphs for radiation
ggline(temp_carrizo2022, x = "day", y = "intensity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Radiation (lum/ft²)", legend.title= "Fabric", legend="right")+ ggtitle('Carrizo 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) ## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 1788 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_carrizo2023, x = "day", y = "intensity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Radiation (lum/ft²)", legend.title= "Fabric", legend="right")+ ggtitle('Carrizo 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) ## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 24193 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_carrizo2023_winter, x = "day", y = "intensity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Radiation (lum/ft²)", legend.title= "Fabric", legend="right")+ ggtitle('Carrizo Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) 
ggline(temp_mojave2022, x = "day", y = "intensity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Radiation (lum/ft²)", legend.title= "Fabric", legend="right")+ ggtitle('Mojave 2022')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) 
ggline(temp_mojave2023, x = "day", y = "intensity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Radiation (lum/ft²)", legend.title= "Fabric", legend="right")+ ggtitle('Mojave 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) ## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 17457 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_mojave2023_winter, x = "day", y = "intensity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Radiation (lum/ft²)", legend.title= "Fabric", legend="right")+ ggtitle('Carrizo Winter 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) 
###line graphs for humidity. Really only got good humidity for Carrizo and Mojave spring/summer 2023.
ggline(temp_carrizo2023, x = "day_humidity", y = "humidity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Humidity (%)", legend.title= "Fabric", legend="right")+ ggtitle('Carrizo 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) + xlim(0, 30)## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 28875 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 16 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 16 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggline(temp_mojave2023, x = "day_humidity", y = "humidity", color = "microsite",
add = "mean_se", shape = "microsite", xlab = "Day", ylab = "Humidity (%)", legend.title= "Fabric", legend="right")+ ggtitle('Mojave 2023')+ theme(panel.border = element_rect(color = "black",
fill = NA,
size = 1)) + theme (axis.text.y = element_text(margin = margin(r=0))) + xlim(0, 30)## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning in base::min(x, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in base::max(x, na.rm = TRUE): no non-missing arguments to max;
## returning -Inf## Warning in stats::qt(ci/2 + 0.5, data_sum$length - 1): NaNs produced## Warning: Removed 26677 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some###Levene's Test for Homogeneity of Variance
leveneTest(temp ~ microsite, temp_carrizo2022) #variation in temperature between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 565.19 < 2.2e-16 ***
## 31538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(temp ~ microsite, temp_carrizo2023)#variation in temperature between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 165.33 < 2.2e-16 ***
## 28581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(temp ~ microsite, temp_carrizo2023_winter)#variation in temperature between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 10.64 5.946e-07 ***
## 2666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(temp ~ microsite, temp_mojave2022)#variation in temperature between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 103.65 < 2.2e-16 ***
## 10292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(temp ~ microsite, temp_mojave2023)#variation in temperature between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1350.7 < 2.2e-16 ***
## 26673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(temp ~ microsite, temp_mojave2023_winter)#variation in temperature between groups is not significant ## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.3957 0.7561
## 2059leveneTest(intensity ~ microsite, temp_carrizo2022) #variation in intensity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 898.54 < 2.2e-16 ***
## 31538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(intensity ~ microsite, temp_carrizo2023)#variation in intensity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 146.4 < 2.2e-16 ***
## 28581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(intensity~ microsite, temp_carrizo2023_winter)#variation in intensity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 44.263 < 2.2e-16 ***
## 2666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(intensity ~ microsite, temp_mojave2022)#variation in intensity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 222.62 < 2.2e-16 ***
## 10292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(intensity~ microsite, temp_mojave2023)#variation in intensity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 124.12 < 2.2e-16 ***
## 26673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(intensity ~ microsite, temp_mojave2023_winter)#variation in intensity between groups is significant ## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 6.3414 0.0002811 ***
## 2059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(humidity ~ microsite, temp_carrizo2023)#variation in humidity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 226.62 < 2.2e-16 ***
## 24187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(humidity~ microsite, temp_mojave2023)#variation in humidity between groups is significant## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 2.3678 0.06872 .
## 17453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create microsite map for appendix
library(ggmap)## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## Stadia Maps' Terms of Service: <https://stadiamaps.com/terms-of-service/>
## OpenStreetMap's Tile Usage Policy: <https://operations.osmfoundation.org/policies/tiles/>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.register_google(key="AIzaSyBpfKtYrkYVS3LEJSjV1cIHeYrxJPsPX4U")
carrizo_mojave<-get_map(location = c(lon = -119.417931, lat = 36.778259), zoom = 6, maptype = "satellite")## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=36.778259,-119.417931&zoom=6&size=640x640&scale=2&maptype=satellite&language=en-EN&key=xxx>lat_long<- read.csv("C:/Users/Nargol Ghazian/Desktop/PhD-Shelter-Shrub-Climate/shelters/camtraps/Sites 2023.csv")
Carrizo <- get_map(location = c(lon = -119.7929080, lat = 35.1913582), zoom = 11, maptype = "terrain")## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=35.191358,-119.792908&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx>Tecopa<- get_map(location = c(lon = -116.226389, lat = 35.848333), zoom = 13, maptype = "terrain")## ℹ <https://maps.googleapis.com/maps/api/staticmap?center=35.848333,-116.226389&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx>ggmap(carrizo_mojave) +
geom_point(data= lat_long, aes(x=long, y=lat, color = microsite), alpha = 6/10, size =3, show.legend = TRUE) +
labs(title = "California, USA", x = "Longitude", y = "Latitude")+ theme(legend.position = "none")
ggmap(Carrizo) +
geom_point(data= lat_long, aes(x=long, y=lat, color = microsite), alpha = 6/10, size =3, show.legend = TRUE) +
labs(title = "Carrizo Plain National Monument", x = "Longitude", y = "Latitude")+ theme(legend.position = "none")## Warning: Removed 40 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggmap(Tecopa) +
geom_point(data= lat_long, aes(x=long, y=lat, color = microsite), alpha = 6/10, size =3, show.legend = TRUE) +
labs(title = "Tecopa", x = "Longitude", y = "Latitude")+ theme(legend.position = "none")## Warning: Removed 40 rows containing missing values or values outside the scale range
## (`geom_point()`).
```